-
Marc Hanheide, Nick Hawes, Jeremy Wyatt, Moritz Göbelbecker, Michael Brenner, Kristoffer Sjöö, Alper Aydemir, Patric Jensfelt, Hendrik Zender and Geert-Jan Kruijff.
A Framework for Goal Generation and Management.
In
Proceedings of the AAAI Workshop on Goal-Directed Autonomy.
2010.
(Show abstract)
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Goal-directed behaviour is often viewed as an
essential char- acteristic of an intelligent system, but
mechanisms to generate and manage goals are often overlooked. This
paper addresses this by presenting a framework for autonomous goal
gener- ation and selection. The framework has been implemented as
part of an intelligent mobile robot capable of exploring unknown
space and determining the category of rooms au- tonomously. We
demonstrate the efficacy of our approach by comparing the
performance of two versions of our inte- grated system: one with
the framework, the other without. This investigation leads us
conclude that such a framework is desirable for an integrated
intelligent system because it re- duces the complexity of the
problems that must be solved by other behaviour-generation
mechanisms, it makes goal- directed behaviour more robust in the
face of a dynamic and unpredictable environments, and it provides
an entry point for domain-specific knowledge in a more general
system.
-
Michael Brenner.
Creating Dynamic Story Plots with Continual Multiagent Planning.
In
Maria Fox and David Poole (eds.),
Proceedings of the Twenty-Fourth AAAI Conference on Artificial
Intelligence (AAAI
2010), pp. 1517-1522.
AAAI Press 2010.
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An AI system that is to create a story (autonomously or in
interaction with human users) requires capabilities from many
subfields of AI in order to create characters that themselves
appear to act intelligently and believably in a coherent story
world. Specifically, the system must be able to reason about
the physical actions and verbal interactions of the characters
as well as their perceptions of the world. Furthermore it must
make the characters act believably--i.e. in a goal-directed
yet emotionally plausible fashion. Finally, it must cope with
(and embrace!) the dynamics of a multiagent environment where
beliefs, sentiments, and goals may change during the course of
a story and where plans are thwarted, adapted and dropped all
the time. In this paper, we describe a representational and
algorithmic framework for modelling such dynamic story worlds,
Continual Multiagent Planning. It combines continual planning
(i.e. an integrated approach to planning and execution) with a
rich description language for modelling epistemic and
affective states, desires and intentions, sensing and
communication. Analysing story examples generated by our
implemented system we show the benefits of such an integrated
approach for dynamic plot generation.
-
Michael Brenner.
Dynamic Plot Generation by Continual Multiagent Planning (extended abstract).
In
Proceedings of the 9th Int. Joint Conf. on Autonomous Agents and Multiagent Systems
(AAMAS 2010).
2010.
(Show abstract)
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(PDF)
(BIB)
We describe how, by modelling plot generation as a Continual
Multiagent Planning process, dynamic stories can be generated in
which characters not only inteleave perception, action and
interaction, but in which also beliefs and motivations may change
repeatedly, thus driving the plot forward.
-
Moritz Göbelbecker, Thomas Keller, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
Coming Up with Good Excuses: What To Do When No Plan Can be Found.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 81-88.
AAAI Press 2010.
(Show abstract)
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When using a planner-based agent architecture, many things can
go wrong. First and foremost, an agent might fail to execute
one of the planned actions for some reasons. Even more
annoying, however, is a situation where the agent is
incompetent, i.e., unable to come up with a plan. This
might be due to the fact that there are principal reasons that
prohibit a successful plan or simply because the task's
description is incomplete or incorrect. In either case, an
explanation for such a failure would be very helpful. We will
address this problem and provide a formalization of coming
up with excuses for not being able to find a plan. Based
on that, we will present an algorithm that is able to find
excuses and demonstrate that such excuses can be found in
practical settings in reasonable time.
-
Nick Hawes, Marc Hanheide, Kristoffer Sjöö, Alper Aydemir, Patric Jensfelt, Moritz Göbelbecker, Michael Brenner, Hendrik Zender, Pierre Lison, Ivana Kruijff-Korbayov, Geert-Jan M. Kruijff and Michael Zillich.
Dora The Explorer: A Motivated Robot.
In
Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010).
2010.
(Show abstract)
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Dora the Explorer is a mobile robot with a sense of
curios- ity and a drive to explore its world. Given an incomplete
tour of an indoor environment, Dora is driven by internal
motivations to probe the gaps in her spatial knowledge. She
actively explores regions of space which she hasn't previously
visited but which she expects will lead her to further unex-
plored space. She will also attempt to determine the cate- gories
of rooms through active visual search for functionally important
objects, and through ontology-driven inference on the results of
this search.
-
Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner and Bernhard Nebel.
Semantic Attachments for Domain-Independent Planning Systems.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 114-121.
AAAI Press 2009.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Solving real-world problems using symbolic planning often
requires a simplified formulation of the original problem,
since certain subproblems cannot be represented at all or only
in a way leading to inefficiency. For example, manipulation
planning may appear as a subproblem in a robotic planning
context or a packing problem can be part of a logistics
task. In this paper we propose an extension of PDDL for
specifying semantic attachments. This allows the evaluation of
grounded predicates as well as the change of fluents by
externally specified functions. Furthermore, we describe a
general schema of integrating semantic attachments into a
forward-chaining planner and report on our experience of
adding this extension to the planners FF and Temporal Fast
Downward. Finally, we present some preliminary experiments
using semantic attachments.
-
Michael Brenner and Bernhard Nebel.
Continual Planning and Acting in Dynamic Multiagent Environments.
Journal of Autonomous Agents
and Multiagent Systems 19 (3), pp. 297-331. 2009.
(Show abstract)
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(PDF)
(BIB)
In order to behave intelligently, artificial agents must be able
to deliberatively plan their future actions. Unfortunately,
realistic agent environments are usually highly dynamic and only
partially observable, which makes planning computationally hard. For
most practical purposes this rules out planning techniques that
account for all possible contingencies in the planning process.
However, many agent environments permit an alternative approach,
namely continual planning, i.e. the interleaving of planning with
acting and sensing.
This paper presents a new principled approach to continual
planning that describes why and when an agent should switch between
planning and acting. The resulting continual planning algorithm
enables agents to deliberately postpone parts of their planning
process and instead actively gather missing information that is
relevant for the later refinement of the plan. To this end, the
algorithm explictly reasons about the knowledge (or lack thereof) of
an agent and its sensory capabilities. These concepts are modelled
in the planning language MAPL. Since in many environments the major
reason for dynamism is the behaviour of other agents, MAPL can also
model multiagent environments, common knowledge among agents, and
communicative actions between them. For Continual Planning, MAPL
introduces the concept of of assertions, abstract actions that
substitute yet unformed subplans.
To evaluate our continual planning approach empirically we have
developed MAPSIM, a simulation environment that automatically builds
multiagent simulations from formal MAPL domains. Thus, agents can
not only plan, but also execute their plans, perceive their
environment, and interact with each other. Our experiments show
that, using continual planning techniques, deliberate action planning
can be used efficiently even in complex multiagent environments.
-
Geert-Jan Kruijff and Michael Brenner.
Phrasing Questions.
In
AAAI Spring Symposium on Agents that Learn from Human Teachers.
2009.
-
Michael Brenner.
Continual Collaborative Planning for Mixed-Initiative Action and
Interaction.
In
Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS
2008).
2008.
(Show abstract)
(Hide abstract)
(PDF)
Multiagent environments are often highly dynamic and only
partially observable which makes deliberative action planning
computationally hard. In many such environments, however, agents can
take a more proactive approach and suspend planning for partial plan
execution, especially for active information gathering and
interaction with others. This paper presents a new algorithm for
Continual Collaborative Planning (CCP) that enables agents to
deliberately interleave planning, acting, perception and
communication. Our implementation of CCP has been evaluated with
MAPSIM, a tool that automatically generates multiagent simulations
from formal multiagent planning (MAP) domains. For different such
simulations, we show how CCP leads to collaborative planning and
acting and, despite minimal linguistic capabilities, to fairly
natural dialogues between agents.
-
Michael Brenner and Ivana Kruijff-Korbayova.
A Continual Multiagent Planning Approach to Situated
Dialogue.
In
Proceedings of the 12th Workshop on the Semantics and Pragmatics of
Dialogue (LonDial
2008).
2008.
(Show abstract)
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(PDF)
Situated dialogue is usually tightly integrated with behavior
planning, physical action and perception. This paper presents an
algorithmic framework, Continual Collaborative Planning (CCP), for
modeling this kind of integrated behavior and shows how CCP agents
naturally blend physical and communicative actions. For experiments
with conversational CCP agents we have developed MAPSIM, a software
environment that can generate multiagent simulations from formal
multiagent planning problems automatically. MAPSIM permits comparison
of CCP-based dialogue strategies on a wide range of domains and
problems without domain-specific programming. Despite their
linguistic capabilities being limited MAPSIM agents can already
engage in fairly realistic situated dialogues. Our ongoing work is
taking this approach from simulation to real human-robot interaction.
-
Geert-Jan Kruijff, Michael Brenner and Nick Hawes.
Continual Planning for Cross-Modal Situated Clarification in
Human-Robot Interaction.
In
Proceedings of the 17th IEEE International Symposium on Robots and
Human Interactive Communication
(RO-MAN 2008).
2008.
(Show abstract)
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(PDF)
Current robots do not fully understand the world they are
situated in, including what humans talk to them about. A fundamental
problem in robotics is thus how a robot can clarify such a lack of
understanding. This paper addresses the question of how a robot can
create a plan for resolving a need for clarification. This paper
characterizes situated clarification as an information need which may
arise in any sensory-motoric modality interpreting the situated
context of the robot, or any deliberative modality referring to that
context. The paper then focuses on how, once a clarification need has
been identified, the robot can create a plan in which one or more
modalities are involved in resolving it. Modalities are involved on
the basis of the types of information they can provide. These
information types are identified in the ontologies the modalities use
to interconnect their content with content of other modalities
("information fusion"). We take a continual approach to planning and
execution monitoring. This provides the abiltity to re-plan depending
on modality availability and success in resolving (part of) a
clarification need. We illustrate our implementation of this approach
with several examples from our system.
-
Paul Plöger, Kai Pervölz, Christoph Mies, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
The DESIRE Service Robotics Initiative.
Künstliche Intelligenz 08 (4), pp. 29-32. 2008.
(Show abstract)
(Hide abstract)
We present some advanced hardware units and an appropriate
component based SW architecture for DESIRE. As an example we
describe the integration of a enhanced AI task planner which
allows for higher flexibility and dependability during complex
task execution.
-
Patrick Eyerich, Michael Brenner and Bernhard Nebel.
On the Complexity of Planning Operator Subsumption.
In
Proceedings of the Eleventh International Conference on
Principles of Knowledge Representation and Reasoning
(KR
2008), pp. 518-527.
AAAI Press 2008.
(Show abstract)
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(PDF)
(BIB)
Formal action models play a central role in several subfields of
AI because they are used to model application domains, e.g., in
automated planning. However, there are hitherto no automated
methods for relating such domain models to each other, in
particular for checking whether one is a specialization or
generalization of the other. In this paper, we introduce two kinds
of subsumption relations between operators, both of which are
suitable for modeling and verifying hierarchies between actions
and operators: applicability subsumption considers an action to be
more general than another if the latter can be replaced by the
first at each point in each sound sequence of actions; abstraction
subsumption exploits relations between actions from an ontological
point of view. For both kinds of subsumption, we prove complexity
results for verifying operator subsumption in three important
subclasses: The problems are NP-complete when the expressiveness
of the operators is restricted to the well-known basic STRIPS
formalism, Sigma_p_2-complete when we admit boolean logical operators
and undecidable when the full power of the planning language ADL
is permitted.
-
Michael Brenner.
Situation-Aware Interpretation, Planning and Execution of User Commands by Autonomous Robots.
In
Proceedings of the 16th IEEE International Symposium on Robots and
Human Interactive Communication
(ROMAN 2007).
Jeju, Korea 2007.
(PDF)
-
Nick Hawes, Aaron Sloman, Jeremy Wyatt, Michael Zillich, Henrik Jacobsson, Geert-Jan Kruijff, Michael Brenner, Gregor Berginc and Danijel Skocaj.
Towards an Integrated Robot with Multiple Cognitive Functions.
In
Proceedings of the 22nd Conference on Artificial Intelligence
(AAAI 2007).
Vancouver, Canada 2007.
(PDF)
-
Geert-Jan Kruijff and Michael Brenner.
Modelling Spatio-Temporal Comprehension in Situated Human-Robot Dialogue as Reasoning About Intentions and Plans.
In
AAAI Spring Symposium on Intentions.
2007.
-
Michael Brenner, Nick Hawes, John Kelleher and Jeremy Wyatt.
Mediating Between Qualitative and Quantitative Representations for
Task-Orientated Human-Robot Interaction.
In
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007).
Hyderabad, India 2007.
(PDF)
-
Michael Brenner and Bernhard Nebel.
Continual Planning and Acting in Dynamic Multiagent Environments.
In
Proceedings of the International Symposium on Practical Cognitive Agents and Robots.
Perth, Australia 2006.
(PDF)
-
Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger, Joerg Stueckler and Bernhard Nebel.
Successful Search and Rescue in Simulated Disaster Areas.
In
Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
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(PDF)
RoboCupRescue Simulation is a large-scale multi-agent simulation
of urban disasters where, in order to save lives and minimize damage, rescue
teams must effectively cooperate despite sensing and communication limitations.
This paper presents the comprehensive search and rescue approach of the ResQ
Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup
2004.
Specific contributions include the predictions of travel costs and civilian lifetime,
the efficient coordination of an active disaster space exploration, as well as
an any-time rescue sequence optimization based on a genetic algorithm.
We compare the performances of our team and others in terms of their capability
of extinguishing fires, freeing roads from debris, disaster space exploration, and
civilian rescue. The evaluation is carried out with information extracted from
simulation log files gathered during RoboCup 2004. Our results clearly explain
the success of our team, and also confirm the scientific approaches proposed in
this paper.
-
Michael Brenner, Nanda Wijermans, Timo Nuessle and Bart de Boer.
Simulating and Controlling Civilian Crowds in Robocup Rescue.
In
RoboCup.
Osaka, Japan 2005.
Winner of the RoboCupRescue Infrastructure Competition 2005.
-
Michael Brenner.
Planning for Multiagent Environments: From Individual Perceptions to Coordinated Execution.
In
Workshop on Multiagent Planning and Scheduling (ICAPS 2005).
Monterey, USA 2005.
(PDF)
-
Timo Nuessle, Alexander Kleiner and Michael Brenner.
Approaching Urban Disaster Reality: The ResQ Firesimulator.
In
Proceedings of the International RoboCup Symposium '04.
Lisbon, Portugal 2004.
(PDF)
-
Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger and Joerg Stueckler.
ResQ Freiburg: Team Description and Evaluation, Team Description Paper, Rescue Simulation League.
In
CDROM Proceedings of the International RoboCup Symposium '04.
Lisbon, Portugal 2004.
(PDF)
-
Michael Brenner.
Multiagent Planning with Partially Ordered Temporal Plans.
In
Proceedings of IJCAI'03.
Acapulco, Mexico 2003.
(PDF)
-
Michael Brenner.
A Multiagent Planning Language.
In
Workshop on PDDL (ICAPS 2003).
Trento, Italy 2003.
(PDF)
-
Michael Brenner.
A Formal Model for Planning with Time and Resources in Concurrent Domains.
In
Workshop on Planning with Resources (IJCAI 2001).
Seattle, Washington, USA 2001.
(PS.GZ)
-
Christian Dornhege and Alexander Kleiner.
A Frontier-Void-Based Approach for Autonomous Exploration in 3D.
In
Proceedings of the IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR).
2011.
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We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform equipped with a manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.
-
Matthias Westphal, Christian Dornhege, Stefan Wölfl, Marc Gissler and Bernhard Nebel.
Guiding the Generation of Manipulation Plans by Qualitative Spatial Reasoning.
Spatial Cognition & Computation: An Interdisciplinary Journal 11 (1), pp. 75-102. 2011.
(BIB)
-
R. Kümmerle, B. Steder, Christian Dornhege, Alexander Kleiner, G. Grisetti and W. Burgard.
Large Scale Graph-based SLAM using Aerial Images as Prior Information.
Autonomous Robots 30, pp. 25-39. 2011.
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The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.
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Kai M. Wurm, Christian Dornhege, Patrick Eyerich, Cyrill Stachniss, Bernhard Nebel and Wolfram Burgard.
Coordinated Exploration with Marsupial Teams of Robots using Temporal Symbolic Planning.
In
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010).
2010.
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The problem of autonomously exploring an environment with a team
of robots received considerable attention in the past. However,
there are relatively few approaches to coordinate teams of
robots that are able to deploy and retrieve other
robots. Efficiently coordinating the exploration with such
marsupial robots requires advanced planning mechanisms that are
able to consider symbolic deployment and retrieval actions. In
this paper, we propose a novel approach for coordinating the
exploration with marsupial robot teams. Our method integrates a
temporal symbolic planner that explicitly considers deployment
and retrieval actions with a traditional cost-based assignment
procedure. Our approach has been implemented and evaluated in
several simulated environments and with varying team sizes. The
results demonstrate that our proposed method is able to
coordinate marsupial teams of robots to efficiently explore
unknown environments.
-
Alexander Kleiner and Christian Dornhege.
Mapping for the Support of First Responders in Critical Domains.
Journal of Intelligent and Robotic Systems (JINT), pp. 1-29. 2010.
(Show abstract)
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(PDF)
(BIB)
In critical domains such as urban search and rescue (USAR), and bomb disposal, the deployment of teleoperated robots is essential to reduce the risk of first responder personnel. Teleoperation is a difficult task, particularly when controlling robots from an isolated safety zone. In general, the operator has to solve simultaneously the problems of mission planning, target identification, robot navigation, and robot control. We introduce a system to support teleoperated navigation with real-time mapping consisting of a two-step scan matching method that re-considers data associations during the search. The algorithm processes data from laser range finder and gyroscope only, thereby it is independent from the robot platform. Furthermore, we introduce a user-guided procedure for improving the global consistency of maps generated by the scan matcher. Globally consistent maps are computed by a graph-based maximum likelihood method that is biased by localizing crucial parts of the scan matcher trajectory on a prior given geo-tiff image. The approach has been implemented as an embedded system and extensively tested on robot platforms designed for teleoperation in critical situations, such as bomb disposal. Furthermore, the system was evaluated in a test maze by first responders during the Disaster City event in Texas 2008.
-
Marc Gissler, Christian Dornhege, Bernhard Nebel and Matthias Teschner.
Deformable Proximity Queries and their Application in Mobile Manipulation Planning.
In
Symposium on Visual Computing (ISVC 2009), pp. 79-88.
AAAI Press 2009.
(Show abstract)
(Hide abstract)
(BIB)
-
Alexander Kleiner and Christian Dornhege.
Operator-Assistive Mapping in Harsh Environments.
In
IEEE International Workshop on Safety, Security and Rescue Robotics
(SSRR 2009), pp. 1-6.
2009.
(Show abstract)
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(PDF)
(BIB)
Teleoperation is a difficult task, particularly when
controlling robots from an isolated operator station.
In general, the operator has to solve nearly blindly the problems of mission
planning, target identification, robot navigation, and robot control at the same time.
The goal of the proposed system is to support teleoperated navigation
with real-time mapping.
We present a novel scan matching technique that re-considers data
associations during the search, enabling robust pose estimation even under
varying roll and pitch angle of the robot enabling mapping
on rough terrain.
The approach has been implemented as an embedded system and extensively tested
on robot platforms designed for teleoperation in critical situations, such as bomb
disposal.
Furthermore,
the system has been evaluated in a test maze by first responders during
the Disaster City event in Texas 2008.
Finally, experiments conducted within different environments show that
the system yields comparably accurate maps in real-time when
compared to higher sophisticated offline methods, such as Rao-Blackwellized SLAM.
-
Christian Dornhege, Marc Gissler, Matthias Teschner and Bernhard Nebel.
Integrating Symbolic and Geometric Planning for Mobile Manipulation.
In
IEEE International Workshop on Safety, Security and Rescue Robotics
(SSRR 2009).
2009.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Mobile manipulation requires to solve multiple subproblems.
One is planning in high-dimensional configuration spaces, that we approach in this work.
We decompose the manipulation problem into a symbolic and a geometric part.
The symbolic part is implemented as a classical symbolic planner that
tightly integrates a geometric planner enabling us to efficiently generate correct
plans.
A probabilistic roadmap planner constitutes the geometric part.
During the computation of the roadmap we utilize proximity queries to determine non-colliding configurations and to verify collision-free paths between configurations accurately and efficiently.
We demonstrate experiments in two scenarios, one of these being the manipulator dexterity test scenario that was
used in NIST's response robot evaluation in Disaster City.
-
Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner and Bernhard Nebel.
Semantic Attachments for Domain-Independent Planning Systems.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 114-121.
AAAI Press 2009.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Solving real-world problems using symbolic planning often
requires a simplified formulation of the original problem,
since certain subproblems cannot be represented at all or only
in a way leading to inefficiency. For example, manipulation
planning may appear as a subproblem in a robotic planning
context or a packing problem can be part of a logistics
task. In this paper we propose an extension of PDDL for
specifying semantic attachments. This allows the evaluation of
grounded predicates as well as the change of fluents by
externally specified functions. Furthermore, we describe a
general schema of integrating semantic attachments into a
forward-chaining planner and report on our experience of
adding this extension to the planners FF and Temporal Fast
Downward. Finally, we present some preliminary experiments
using semantic attachments.
-
Moritz Göbelbecker and Christian Dornhege.
Realistic Cities in Simulated Environments - An Open Street Map to Robocup Rescue Converter.
In
Online-Proceedings of the Fourth International Workshop on Synthetic Simulation
and Robotics to Mitigate Earthquake Disaster (SRMED 2009).
2009.
(Show abstract)
(Hide abstract)
(PDF)
A general problem when developing large scale disaster simulation environments is to acquire GIS data.
In this work, we tackle the problem of map generation from public sources.
Usually the major problem is not only the data conversion itself, but to get access to the data at all.
We solve this problem by using the website OpenStreetMap.org, that provides mapping data for the whole world in a wiki-style concept, as our source of data,
thus being able to generate maps for almost any city.
The data is converted to the format required by the Robocup Rescue Simulation System, enabling simulations
on various real-world scenarios.
-
Rainer Kümmerle, Bastian Steder, Christian Dornhege, Michael Ruhnke, Giorgio Grisetti, Cyrill Stachniss and Alexander Kleiner.
On Measuring the Accuracy of SLAM Algorithms.
Autonomous Robots 27 (4), pp. 387-407. 2009.
(Show abstract)
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(PDF)
(BIB)
In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.
We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.
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Wolfram Burgard, Cyrill Stachniss, Giorgio Grisetti, Bastian Steder, Rainer Kümmerle, Christian Dornhege, Michael Ruhnke, Alexander Kleiner and Juan D. Tardos.
A Comparison of SLAM Algorithms Based on a Graph of Relations.
In
Proceedings of the 2009 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2009), pp. 2089-2095.
IEEE 2009.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
In this paper, we address the problem of creating
an objective benchmark for comparing SLAM approaches.
We propose a framework for analyzing the results of SLAM
approaches based on a metric for measuring the error of the
corrected trajectory. The metric uses only relative relations
between poses and does not rely on a global reference frame.
The idea is related to graph-based SLAM approaches in
the sense that it considers the energy needed to deform the
trajectory estimated by a SLAM approach to the ground
truth trajectory. Our method enables us to compare SLAM
approaches that use different estimation techniques or different
sensor modalities since all computations are made based on the
corrected trajectory of the robot. We provide sets of relative
relations needed to compute our metric for an extensive set
of datasets frequently used in the SLAM community. The
relations have been obtained by manually matching laser-range
observations. We believe that our benchmarking framework
allows the user an easy analysis and objective comparisons
between different SLAM approaches.
-
Rainer Kümmerle, Bastian Steder, Christian Dornhege, Alexander Kleiner, Giorgio Grisetti and Wolfram Burgard.
Large Scale Graph-based SLAM using Aerial Images as Prior Information.
In
Proceedings of 2009 Robotics: Science and Systems (RSS 2009).
2009.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
To effectively navigate in their environments and accurately
reach their target locations, mobile robots require a globally
consistent map of the environment. The problem of learning a map
with a mobile robot has been intensively studied in the past and
is usually referred to as the simultaneous localization and
mapping (SLAM) problem. However, existing solutions to the SLAM
problem typically rely on loop-closures to obtain global
consistency and do not exploit prior information even if it is
available. In this paper, we present a novel SLAM approach that
achieves global consistency by utilizing publicly accessible
aerial photographs as prior information. Our approach inserts
correspondences found between three-dimensional laser range
scans and the aerial image as constraints into a graph-based
formulation of the SLAM problem. We evaluate our algorithm based
on large real-world datasets acquired in a mixed in- and outdoor
environment by comparing the global accuracy with
state-of-the-art SLAM approaches and GPS. The experimental
results demonstrate that the maps acquired with our method show
increased global consistency.
-
Christian Dornhege and Alexander Kleiner.
Fully autonomous planning and obstacle negotiation on rough terrain using behavior maps.
In
Video Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2007).
San Diego, California 2007.
-
Christian Dornhege and Alexander Kleiner.
Behavior maps for online planning of obstacle negotiation and climbing on rough terrain.
In
Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2007), pp. 3005-3011.
2007.
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To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.
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Alexander Kleiner and Christian Dornhege.
Real-time Localization and Elevation Mapping within Urban Search and Rescue Scenarios.
Journal of Field Robotics 24, pp. 723-745. 2007.
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Urban Search And Rescue (USAR) is a time critical task. Rescue teams have to explore a large terrain within a short amount of time in order to locate survivors after a disaster. One goal in Rescue Robotics is to have a team of heterogeneous robots that explore autonomously, or partially guided by an incident commander, the disaster area. Their task is to jointly create a map of the terrain and to register victim locations, which can further be utilized by human task forces for rescue. Basically, the robots have to solve autonomously in real-time the problem of Simultaneous Localization and Mapping (SLAM), consisting of a continuous state estimation problem and a discrete data association problem. Extraordinary circumstances after a real disaster make it very hard to apply common techniques. Many of these have been developed under strong assumptions, for example, they require polygonal structures, such as typically found in office-like environments. Furthermore, most techniques are not deployable in real-time. In this paper we propose real-time solutions for localization and mapping, which all have been extensively evaluated within the test arenas of the National Institute of Standards and Technology (NIST). We specifically deal with the problems of vision-based pose tracking on tracked vehicles, the building of globally consistent maps based on a network of RFID tags, and the building of elevation maps from readings of a tilted Laser Range Finder (LRF). Our results show that these methods lead under modest computational requirements to good results within the utilized testing arenas.
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Alexander Kleiner, Christian Dornhege and Dali Sun.
Mapping disaster areas jointly: RFID -Coordinated SLAM by Humans and Robots.
In
Proceedings of the IEEE International Workshop on Safety, Security
and Rescue Robotics (SSRR 2007), pp. 1-6.
2007.
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We consider the problem of jointly performing SLAM by humans and robots in Urban Search And Rescue (USAR) scenarios. In this context, SLAM is a challenging task. First, places are hardly re-observable by vision techniques since visibility might be affected by smoke and fire. Second, loop-closure is cumbersome due to the fact that firemen will intentionally try to avoid performing loops when facing the reality of emergency response, e.g.USAR, while they are searching for victims. Furthermore, there might be places that are only accessible to robots, making it necessary to integrate humans and robots into one team for mapping the area after a disaster. In this paper, we introduce a method for jointly correcting individual trajectories of humans and robots by utilizing RFID technology for data association. Hereby the poses of humans and robots are tracked by a PDR (Pedestrian Dead Reckoning), and slippage sensitive odometry, respectively. We conducted extensive experiments with a team of humans, and a human-robot team within a semi-outdoor environment. Results from these experiments show that the introduced method allows to improve single trajectories based on the joint graph, even if they do not contain any loop.
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Alexander Kleiner, Christian Dornhege, Rainer Kuemmerle, Michael Ruhnke, Bastian Steder, Bernhard Nebel, Patrick Doherty, Mariusz Wzorek, Piotr Rudol, Gianpaolo Conte, S. Durante and D. Lundstrom.
RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
In
CDROM Proceedings of the International RoboCup Symposium '05.
Bremen, Germany 2006.
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This paper describes the approach of the RescueRobots Freiburg team,
which is a team of students from the University of Freiburg that originates from
the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team
(RoboCupRescue Simulation). Furthermore we introduce linkMAV, a micro aerial
vehicle platform.
Our approach covers RFID-based SLAM and exploration, autonomous detection
of relevant 3D structures, visual odometry, and autonomous victim identification.
Furthermore, we introduce a custom made 3D Laser Range Finder (LRF) and a
novel mechanism for the active distribution of RFID tags.
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Christian Dornhege and Alexander Kleiner.
Visual Odometry for Tracked Vehicles.
In
Proceedings of the IEEE International Workshop on Safety, Security and Rescue Robotics (SSRR 2006).
2006.
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Localization and mapping on autonomous robots typically requires a good pose estimate, which is hard to acquire if the vehicle is tracked. In this paper we describe a solution to the pose estimation problem by utilizing a consumer-quality camera and an Inertial Measurement Unit (IMU). The basic idea is to continuously track salient features with the KLT feature tracker over multiple images taken by the camera and to extract from the tracked features image vectors resulting from the robot's motion. Each image vector is taken for a voting that best explains the robot's motion. Image vectors vote according to a previously trained ^\m2102tile coding^\m2112 classificator that assigns to each possible image vector a translation probability. Our results show that the proposed single camera solution leads to sufficiently accurate pose estimates of the tracked vehicle.
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Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger, Joerg Stueckler and Bernhard Nebel.
Successful Search and Rescue in Simulated Disaster Areas.
In
Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
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(PDF)
RoboCupRescue Simulation is a large-scale multi-agent simulation
of urban disasters where, in order to save lives and minimize damage, rescue
teams must effectively cooperate despite sensing and communication limitations.
This paper presents the comprehensive search and rescue approach of the ResQ
Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup
2004.
Specific contributions include the predictions of travel costs and civilian lifetime,
the efficient coordination of an active disaster space exploration, as well as
an any-time rescue sequence optimization based on a genetic algorithm.
We compare the performances of our team and others in terms of their capability
of extinguishing fires, freeing roads from debris, disaster space exploration, and
civilian rescue. The evaluation is carried out with information extracted from
simulation log files gathered during RoboCup 2004. Our results clearly explain
the success of our team, and also confirm the scientific approaches proposed in
this paper.
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Alexander Kleiner, Bastian Steder, Christian Dornhege, Daniel Hoefler, Daniel Meyer-Delius, Johann Prediger, Joerg Stueckler, Kolja Glogowski, Markus Thurner, Matthias Luber, Michael Schnell, Rainer Kuemmerle, Timothy Burk, Tobias Braeuer and Bernhard Nebel.
RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
In
CDROM Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
(Show abstract)
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(PDF)
This paper describes the approach of the RescueRobots Freiburg team.
RescueRobots Freiburg is a team of students from the university of Freiburg, that
originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ
Freiburg team (RoboCupRescue Simulation).
Due to the high versatility of the RoboCupRescue competition we tackle the three
arenas by a a twofold approach: On the one hand we want to introduce robust
vehicles that can safely be teleoperated through rubble and building debris while
constructing three-dimensional maps of the environment. On the other hand we
want to introduce a team of autonomous robots that quickly explore a large terrain
while building a two-dimensional map. This two solutions are particularly wellsuited
for the red and yellow arena, respectively. Our solution for the orange arena
will finally be decided between these two, depending on the capabilities of both
approaches at the venue.
In this paper, we introduce some preliminary results that we achieved so far from
map building, localization, and autonomous victim identification. Furthermore
we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism
for the active distribution of RFID tags.
1 Introduction
RescueRobots Freiburg is a team of students from the university of Freiburg. The team
originates from the former CS Freiburg team[6], which won three times the RoboCup
world championship in the RoboCupSoccer F2000 league, and the ResQ Freiburg team[2],
which won the last RoboCup world championship in the RoboCupRescue Simulation
league. The team approach proposed in this paper is based on experiences gathered at
RoboCup during the last six years.
Due to the high versatility of the RoboCupRescue competition we tackle the three
arenas by a twofold approach: On the one hand we want to introduce a vehicle that
can safely be teleoperated through rubble and building debris while constructing threedimensional
maps of the environment. On the other hand we want to introduce an autonomous
team of robots that quickly explore a large terrain while building a twodimensional
map. This two solutions are particularly well-suited for the red and yellow
arena, respectively. Our solution for the orange arena will finally be decided between
these two, depending on the capabilities of both approaches at the venue.
-
Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger and Joerg Stueckler.
ResQ Freiburg: Team Description and Evaluation, Team Description Paper, Rescue Simulation League.
In
CDROM Proceedings of the International RoboCup Symposium '04.
Lisbon, Portugal 2004.
(PDF)
-
Thomas Keller and Patrick Eyerich.
A Polynomial All Outcome Determinization for Probabilistic
Planning.
In
Fahiem Bacchus, Carmel Domshlak, Stefan Edelkamp and Malte Helmert (eds.),
Proceedings of the 21th International Conference on Automated
Planning and Scheduling (ICAPS 2011), pp. 331-334.
AAAI Press 2011.
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Most predominant approaches in probabilistic planning utilize
techniques from the more thoroughly investigated field of
classical planning by determinizing the problem at hand. In
this paper, we present a method to map probabilistic operators
to an equivalent set of probabilistic operators in a novel
normal form, requiring polynomial time and space. From this,
we directly derive a determinization which can be used for,
\eg, replanning strategies incorporating a classical planning
system. Unlike previously described all outcome
determinizations, the number of deterministic operators is not
exponentially but polynomially bounded in the number of
parallel probabilistic effects, enabling the use of more
sophisticated determinization-based techniques in the future.
-
J. Benton, Patrick Eyerich and Subbarao Kambhampati.
Enhancing Search for Satisficing Temporal Planning with
Objective-driven Decisions.
In
Proceedings of the ICAPS-2011
Workshop on Heuristics for Domain-independent Planning, pp. 59-65.
2011.
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Heuristic best-first search techniques have recently enjoyed
ever-increasing scalability in finding satisficing solutions
to a variety of automated planning problems, and temporal
planning is no different. Unfortunately, achieving efficient
computational performance often comes at the price of clear
guidance toward solution of high quality. This fact is sharp
in the case of many best-first search temporal planners, who
often use a node evaluation function that is mismatched with
the objective function, reducing the likelihood that plans
returned will have a short makespan but increasing search
performance. To help mitigate matters, we introduce a method
that works to progress search on actions declared ``useful''
according to makespan, even when the original search may
ignore the makespan value of search nodes. We study this
method and show that it increases over all plan quality in
most of the benchmark domains from the temporal track of the
2008 International Planning Competition.
-
Kai M. Wurm, Christian Dornhege, Patrick Eyerich, Cyrill Stachniss, Bernhard Nebel and Wolfram Burgard.
Coordinated Exploration with Marsupial Teams of Robots using Temporal Symbolic Planning.
In
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010).
2010.
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The problem of autonomously exploring an environment with a team
of robots received considerable attention in the past. However,
there are relatively few approaches to coordinate teams of
robots that are able to deploy and retrieve other
robots. Efficiently coordinating the exploration with such
marsupial robots requires advanced planning mechanisms that are
able to consider symbolic deployment and retrieval actions. In
this paper, we propose a novel approach for coordinating the
exploration with marsupial robot teams. Our method integrates a
temporal symbolic planner that explicitly considers deployment
and retrieval actions with a traditional cost-based assignment
procedure. Our approach has been implemented and evaluated in
several simulated environments and with varying team sizes. The
results demonstrate that our proposed method is able to
coordinate marsupial teams of robots to efficiently explore
unknown environments.
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Thomas Keller, Patrick Eyerich and Bernhard Nebel.
Task Planning for an Autonomous Service Robot.
In
Rüdiger Dillmann, Jürgen Beyerer, Uwe Hanebeck and Tanja Schultz (eds.),
Proceedings on the 33rd Annual German Conference on Artificial Intelligence (KI 2010), pp. 358-365.
Springer-Verlag 2010.
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In the DESIRE project an autonomous robot capable of performing service tasks in a typical kitchen environment has been developed. The overall system consists of various loosely coupled subcomponents providing particular features like manipulating objects or recognizing and interacting with humans. To bring all these subcomponents together to act as monolithic system, a high-performance planning system has been implemented. In this paper, we present this system’s basic architecture and some advanced extensions necessary to cope with the various challenges arising in dynamic and uncertain environments like those a real world service robot is usually faced with.
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Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem.
In
Maria Fox and David Poole (eds.),
Proceedings of the Twenty-Fourth AAAI Conference on Artificial
Intelligence (AAAI
2010), pp. 51-58.
AAAI Press 2010.
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We consider the stochastic variant of the Canadian
Traveler's Problem, a path planning problem where adverse
weather can cause some roads to be untraversable. The agent
does not initially know which roads can be used. However, it
knows a probability distribution for the weather, and it can
observe the status of roads incident to its location. The
objective is to find a policy with low expected travel cost.
We introduce and compare several algorithms for the
stochastic CTP. Unlike the optimistic approach most
commonly considered in the literature, the new approaches we
propose take uncertainty into account explicitly. We show
that this property enables them to generate policies of much
higher quality than the optimistic one, both theoretically
and experimentally.
-
Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem
(Extended Abstract).
In
Ariel Felner and Nathan Sturtevant (eds.),
Proceedings of the Third Annual Symposium on Combinatorial
Search (SoCS 2010), pp. 147-148.
AAAI Press 2010.
Extended abstract of the AAAI paper by the same name.
(PDF)
-
J. Benton, Kartik Talamadupula, Patrick Eyerich, Robert Mattmüller and Subbarao Kambhampati.
G-value Plateaus: A Challenge for Planning.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 259-262.
AAAI Press 2010.
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Recent years have seen the development of several scalable
planners, many of which follow the string of successes found
in using heuristic, best-first search methods. While this
provides positive reinforcement for continuing work along
these lines, fundamental problems arise when handling
objectives whose value does not change with each search
operation. An extreme case of this occurs when handling the
objective of generating a temporal plan with short
makespan. Typically used heuristic search methods assume
strictly positive edge costs for their guarantees on
completeness and optimality to hold, while the usual
"fattening" and "advance time" steps of heuristic search
algorithms for temporal planning have the potential for
zero-cost edges, resulting in "g-value plateaus". In this
paper we point out some underlying difficulties with using
modern heuristic search methods for optimizing makespan and
discuss how the presence of these problems contributes to the
poor performance of makespan-optimizing heuristic search
planners. To further illustrate this, we show empirical
results on recent benchmarks using a planner made with
makespan optimization in mind.
-
Moritz Göbelbecker, Thomas Keller, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
Coming Up with Good Excuses: What To Do When No Plan Can be Found.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 81-88.
AAAI Press 2010.
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When using a planner-based agent architecture, many things can
go wrong. First and foremost, an agent might fail to execute
one of the planned actions for some reasons. Even more
annoying, however, is a situation where the agent is
incompetent, i.e., unable to come up with a plan. This
might be due to the fact that there are principal reasons that
prohibit a successful plan or simply because the task's
description is incomplete or incorrect. In either case, an
explanation for such a failure would be very helpful. We will
address this problem and provide a formalization of coming
up with excuses for not being able to find a plan. Based
on that, we will present an algorithm that is able to find
excuses and demonstrate that such excuses can be found in
practical settings in reasonable time.
-
Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem.
In
Proceedings of the
ICAPS-2010
Workshop on Planning and Scheduling Under Uncertainty.
2010.
Superseded by the AAAI 2010 paper by the same name.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
We consider the stochastic variant of the Canadian
Traveler's Problem, a path planning problem where adverse
weather can cause some roads to be untraversable. The agent
does not initially know which roads can be used. However, it
knows a probability distribution for the weather, and it can
observe the status of roads incident to its location. The
objective is to find a policy with low expected travel cost.
We introduce and compare several algorithms for the
stochastic CTP. Unlike the optimistic approach most
commonly considered in the literature, the new approaches we
propose take uncertainty into account explicitly. We show
that this property enables them to generate policies of much
higher quality than the optimistic one, both theoretically
and experimentally.
-
Patrick Eyerich, Thomas Keller and Bernhard Nebel.
Combining Action and Motion Planning via Semantic Attachments.
In
Proceedings of the Workshop on Combining Action and Motion Planning at ICAPS 2010
(CAMP 2010), p. 19.
2010.
Extended Abstract.
(PDF)
(BIB)
-
Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner and Bernhard Nebel.
Semantic Attachments for Domain-Independent Planning Systems.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 114-121.
AAAI Press 2009.
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(PDF)
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Solving real-world problems using symbolic planning often
requires a simplified formulation of the original problem,
since certain subproblems cannot be represented at all or only
in a way leading to inefficiency. For example, manipulation
planning may appear as a subproblem in a robotic planning
context or a packing problem can be part of a logistics
task. In this paper we propose an extension of PDDL for
specifying semantic attachments. This allows the evaluation of
grounded predicates as well as the change of fluents by
externally specified functions. Furthermore, we describe a
general schema of integrating semantic attachments into a
forward-chaining planner and report on our experience of
adding this extension to the planners FF and Temporal Fast
Downward. Finally, we present some preliminary experiments
using semantic attachments.
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Patrick Eyerich, Robert Mattmüller and Gabriele Röger.
Using the Context-enhanced Additive Heuristic for Temporal and Numeric Planning.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 130-137.
AAAI Press 2009.
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(slides; PDF)
(BIB)
Planning systems for real-world applications need the ability
to handle concurrency and numeric fluents. Nevertheless, the
predominant approach to cope with concurrency followed by the
most successful participants in the latest International
Planning Competitions (IPC) is still to find a sequential plan
that is rescheduled in a post-processing step. We present
Temporal Fast Downward (TFD), a planning system for temporal
problems that is capable of finding low-makespan plans by
performing a heuristic search in a temporal search space. We
show how the context-enhanced additive heuristic can be
successfully used for temporal planning and how it can be
extended to numeric fluents. TFD often produces plans of high
quality and, evaluated according to the rating scheme of the
last IPC, outperforms all state-of-the-art temporal planning
systems.
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Paul Plöger, Kai Pervölz, Christoph Mies, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
The DESIRE Service Robotics Initiative.
Künstliche Intelligenz 08 (4), pp. 29-32. 2008.
(Show abstract)
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We present some advanced hardware units and an appropriate
component based SW architecture for DESIRE. As an example we
describe the integration of a enhanced AI task planner which
allows for higher flexibility and dependability during complex
task execution.
-
Patrick Eyerich, Michael Brenner and Bernhard Nebel.
On the Complexity of Planning Operator Subsumption.
In
Proceedings of the Eleventh International Conference on
Principles of Knowledge Representation and Reasoning
(KR
2008), pp. 518-527.
AAAI Press 2008.
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(PDF)
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Formal action models play a central role in several subfields of
AI because they are used to model application domains, e.g., in
automated planning. However, there are hitherto no automated
methods for relating such domain models to each other, in
particular for checking whether one is a specialization or
generalization of the other. In this paper, we introduce two kinds
of subsumption relations between operators, both of which are
suitable for modeling and verifying hierarchies between actions
and operators: applicability subsumption considers an action to be
more general than another if the latter can be replaced by the
first at each point in each sound sequence of actions; abstraction
subsumption exploits relations between actions from an ontological
point of view. For both kinds of subsumption, we prove complexity
results for verifying operator subsumption in three important
subclasses: The problems are NP-complete when the expressiveness
of the operators is restricted to the well-known basic STRIPS
formalism, Sigma_p_2-complete when we admit boolean logical operators
and undecidable when the full power of the planning language ADL
is permitted.
-
Jens Claßen, Patrick Eyerich, Gerhard Lakemeyer and Bernhard Nebel.
Towards an Integration of Golog and Planning.
In
Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 1846-1851.
AAAI Press 2007.
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The action language Golog has been applied successfully
to the control of robots, among other
things. Perhaps its greatest advantage is that a
user can write programs which constrain the search
for an executable plan in a xible manner. However,
when general planning is needed, Golog supports
this only in principle, but does not measure
up with state-of-the-art planners. In this paper we
propose an integration of Golog and planning in the
sense that planning problems, formulated as part of
a Golog program, are solved by a modern planner
during the execution of the program. Here we focus
on the ADL subset of the plan language PDDL.
First we show that the semantics of ADL can be
understood as progression in the situation calculus,
which underlies Golog, thus providing us with a
correct embedding of ADL within Golog. We then
show how Golog can be integrated with an existing
ADL planner for closed-world initial databases and
compare the performance of the resulting system
with the original Golog.
-
Patrick Eyerich, Bernhard Nebel, Gerhard Lakemeyer and Jens Classen.
Golog and PDDL: What is the Relative Expressiveness?
In
Proceedings of the International Symposium on Practical Cognitive Agents and Robots (PCAR 2006), pp. 93-104.
University of Western Australia Press 2006.
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(PDF)
(BIB)
Action formalisms such as GOLOG or FLUX have been developed
primarily for representing and reasoning about change in a logical framework.
For this reason, expressivity was the main goal in the development of these formalisms.
In another line of research, efficiency of planning methods was the topmost
goal resulting in the basic STRIPS language, which has only moderate expressivity.
The planning language PDDL developed since 1998 is an extension
of basic STRIPS with many expressive features. Now the interesting question is
how PDDL compares to GOLOG or other action languages from an expressivity
point of view. We will show that a GOLOG fragment, which we call Restricted
Basic Action Theories, is as expressive as the ADL fragment of PDDL. To prove
this equivalence we use the compilation framework. From a practical point of
view, this result can be used for employing efficient planners inside a GOLOG
interpreter.
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Thilo Weigel, Jens-Steffen Gutmann, Markus Dietl, Alexander Kleiner and Bernhard Nebel.
CS Freiburg: Coordinating Robots for Successful Soccer Playing.
IEEE Transactions on Robotics and Automation 18 (5), pp. 685-699. 2002.
(Show abstract)
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Robotic soccer is a challenging research domain because many
different research areas have to be addressed in order to create a
successful team of robot players. This paper presents the CS
Freiburg team, the winner in the middle size league at RoboCup
1998, 2000 and 2001. The paper focuses on multi-agent coordination
for both perception and action. The contributions of this work are
new methods for tracking ball and players observed by multiple
robots, team coordination methods for strategic team formation and
dynamic role assignment, a rich set of basic skills allowing to
respond to large range of situations in an appropriate way, an
action selection method based on behavior networks as well as a
method to learn the skills and their selection. As demonstrated by
evaluations of the different methods and by the success of the team,
these methods permit the creation of a multi-robot group, which is
able to play soccer successfully. In addition, the developed methods
promise to advance the state of the art in the multi-robot field.
-
Markus Dietl, Jens-Steffen Gutmann and Bernhard Nebel.
Cooperative Sensing in Dynamic Environments.
In
Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS-2001).
2001.
(Show abstract)
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(PS.GZ)
(PDF)
This work presents methods for tracking
objects from noisy and unreliable data taken by a team of robots. We
develop a multiobject tracking algorithm based on Kalman filtering
and a singleobject tracking method involving a combination of Kalman
filtering and Markov localization for outlier detection. We apply
these methods in the context of robot soccer for robots participating
in the middlesize league and compare them to a simple averaging
method. Results including situations from real competition games are
presented.
-
Markus Dietl, Jens-Steffen Gutmann and Bernhard Nebel.
CS Freiburg: Global View by Cooperative Sensing.
In
International RoboCup Symposium 2001.
2001.
(Show abstract)
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(PS.GZ)
(PDF)
Global vision systems as found in the small size league are prohibited
in the middle size league. This paper presents methods for creating a
global view of the world by cooperative sensing of a team of
robots. We develop a multiobject tracking algorithm based on Kalman
filtering and a singleobject tracking method involving a combination
of Kalman filtering and Markov localization for outlier detection. We
apply these methods for robots participating in the middlesize league
and compare them to a simple averaging method. Results including
situations from real competition games are presented.
-
Jens-Steffen Gutmann, Thilo Weigel and Bernhard Nebel.
A Fast, Accurate, and Robust Method for Self-Localization in
Polygonal Environments Using Laser-Range-Finders.
Advanced Robotics 14 (8), pp. 651-668. 2001.
(Show abstract)
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(PS.GZ)
(PDF)
Self-localization is important in almost all robotic tasks. For playing an
aesthetic and effective game of robotic soccer, self-localization is a
necessary prerequisite. When we designed our robotic soccer team for
participating in robotic soccer competitions, it turned out that all
existing approaches did not meet our requirements of being fast, accurate,
and robust. For this reason, we developed a new method, which is presented
and analyzed in this paper. This method is one of the key components and is
probably one of the explanations for the success of our team in national and
international competitions. We present also experimental evidence that our
method outperforms other self-localization methods in the RoboCup
environment.
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Thilo Weigel, Willi Auerbach, Markus Dietl, Burkhard Dümler, Jens-Steffen Gutmann, Kornel Marko, Klaus Müller, Bernhard Nebel, Boris Szerbakowski and Maximilian Thiel.
CS Freiburg: Doing the Right Thing in a Group.
In
P. Stone, G. Kraetzschmar and T. Balch (eds.),
RoboCup 2000: Robot Soccer World Cup IV, pp. 52-63.
Springer-Verlag 2001.
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(PS.GZ)
(PDF)
The success of CS Freiburg at RoboCup 2000 can be attributed to an
effective cooperation between players based on sophisticated soccer
skills and a robust and accurate self-localization method. In this
paper, we present our multi-agent coordination approach for both,
action and perception, and our rich set of basic skills which allow
to respond to a large range of situations in an appropriate way.
Furthermore our action selection method based on an extension to
behavior networks is described. Results including statistics from CS
Freiburg final games at RoboCup 2000 are presented.
-
Thilo Weigel, Alexander Kleiner, Florian Diesch, Markus Dietl, Jens-Steffen Gutmann, Bernhard Nebel, Patrick Stiegeler and Boris Szerbakowski.
CS Freiburg 2001.
In
International RoboCup Symposium 2001.
2001.
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(PS.GZ)
(PDF)
The CS Freiburg team has become F2000 champion the third time in the
history of RoboCup. The success of our team can probably be
attributed to its robust sensor interpretation and its team play. In
this paper, we will focus on new developments in our vision system,
in our path planner, and in the cooperation component.
-
Thilo Weigel, Jens-Steffen Gutmann, Bernhard Nebel, Klaus Müller and Markus Dietl.
CS Freiburg: Sophisticated Skills and Effective Cooperation.
In
Proc. European Control Conference (ECC-01).
Porto, Portugal 2001.
(Show abstract)
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(PS.GZ)
(PDF)
The success of CS Freiburg at RoboCup 2000 can be attributed
to a robust and accurate perception approach and an effec
tive cooperation between players based on sophisticated soc
cer skills. In this paper, we present our multiagent coordi
nation approach for both, action and perception, and our rich
set of basic skills which allow to respond to a large range of
situations in an appropriate way. Furthermore, our action se
lection method based on an extension to behavior networks is
described. Results including statistics from CS Freiburg final
games at RoboCup 2000 are presented.
-
Jens-Steffen Gutmann, Wolfgang Hatzack, Immanuel Herrmann, Bernhard Nebel, Frank Rittinger, Augustinus Topor and Thilo Weigel.
The CS Freiburg Team: Playing Robotic Soccer Based on an
Explicit World Model.
AI Magazine 21 (1), pp. 37-46. 2000.
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(preliminary version; PS.GZ)
(preliminary version; PDF)
Robotic soccer is an ideal task to demonstrate new techniques and to explore
new problems. Moreover, problems and solutions can be easily communicated
because soccer is a well-known game. Our intention in building a robotic
soccer team and participating in RoboCup'98 was, first of all, to
demonstrate the usefulness of the self-localization methods we have
developed. Secondly, we wanted to show that playing soccer based on an
explicit world model is much more effective than other methods. Thirdly, we
intended to explore the problem of building and maintaining a global team
world model. As has been demonstrated by the performance of our team, we
were successful on the first two points. Moreover, robotic soccer gave us
the opportunity to study problems in distributed, cooperative sensing.
-
Jens-Steffen Gutmann, Bernhard Nebel and Christian Reetz.
CS Freiburg: Architektur und Aktionsauswahl im
Roboterfuball.
In
Proc. AMS-2000.
2000.
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(PS.GZ)
(PDF)
Roboterfußball ist ein wissenschaftliches
anspruchsvolles Forschungsproblem, das erfordert, Probleme aus den
Bereichen Robotik, Künstliche Intelligenz und Multi-Agenten-Systeme zu
lösen und die Lösungen in einem System zu integrieren, um ein
erfolgreiches Roboterfußballteam zu kreieren. In diesem Papier
beschreiben wir die Schlüsselkomponenten des CS Freiburg
Teams. Dabei fokussieren wir auf die Selbstlokalisation und
Objekterkennungsmethoden und die Integration aller Information in ein
globales Weltmodell. Basierend auf diesem Weltmodell werden dann
Aktionsselektion, Pfadplanung und Kooperation realisiert. Das
resultierende System ist äußerst erfolgreich und hat bisher lediglich
ein Spiel in einem Wettbewerb verloren.
-
Jens-Steffen Gutmann, Thilo Weigel and Bernhard Nebel.
Fast, Accurate, and Robust Self-Localization in Polygonal
Environments.
In
Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS '99).
Kyongju, Korea 1999.
(Show abstract)
(Hide abstract)
(preliminary version; PS.GZ)
Self-localization is important in almost all robotic tasks. For playing an
aesthetic and effective game of robotic soccer, self-localization is a
necessary prerequisite. When we designed our robotic soccer team for
RoboCup'98, it turned out that all existing approaches did not meet our
requirements of being fast, accurate, and robust. For this reason, we
developed a new method, which is presented and analyzed in this paper. We
additionally present experimental evidence that our method outperforms
other methods in the RoboCup environment.
-
Jens-Steffen Gutmann, Wolfgang Hatzack, Immanuel Herrmann, Bernhard Nebel, Frank Rittinger, Augustinus Topor, Thilo Weigel and Bruno Welsch.
The CS Freiburg Robotic Soccer Team: Reliable
Self-Localization, Multirobot Sensor Integration, and Basic Soccer
Skills.
In
M. Asada (ed.),
RoboCup-98: Robot Soccer World Cup II, pp. 93-108.
Springer-Verlag, Berlin, Heidelberg, New York 1999.
(Show abstract)
(Hide abstract)
(PS.GZ)
Robotic soccer is a challenging research domain because problems in
robotics, artificial intelligence, multi-agent systems and real-time
reasoning have to be solved in order to create a successful team of
robotic soccer players. In this paper, we describe the key
components of the CS Freiburg team. We focus on the
self-localization and object recognition method based on using laser
range finders and the integration of all this information into a
global world model. Using the explicit model of the environment
built by these components, we have implemented path planning, simple
ball handling skills and basic multi-agent cooperation. The
resulting system is a very successful robotic soccer team, which has
not lost any game yet.
-
Jens-Steffen Gutmann, Thilo Weigel and Bernhard Nebel.
Fast, Accurate, and Robust Self-Localization in the RoboCup
Environment.
In
Third International Workshop on RoboCup.
1999.
(PS.GZ)
(extended version from Proc. IROS-99; PS.GZ)
-
Jens-Steffen Gutmann, Wolfgang Hatzack, Immanuel Herrmann, Bernhard Nebel, Frank Rittinger, Augustinus Topor and Thilo Weigel.
Reliable Self-Localization, Multirobot Sensor Integration,
Accurate Path-Planning and Basic Soccer Skills: Playing an
Effective Game of Robotic Soccer.
In
Nineth International Conference on Advanced Robotics
(ICAR 1999).
1999.
(Show abstract)
(Hide abstract)
(PS.GZ)
Robotic soccer is a challenging research domain because problems in
robotics, artificial intelligence, multi-agent systems and real-time
reasoning have to be solved in order to create a successful team of
robotic soccer players. In this paper, we describe the key
components of the CS Freiburg team. We focus on the
self-localization and object recognition method based on using laser
range finders and the integration of all this information into a
global world model. Using the explicit model of the environment
built by these components, we have implemented path planning, simple
ball handling skills and basic multi-agent cooperation. The
resulting system is a very successful robotic soccer team, which has
not lost any official game yet.
-
Bernhard Nebel, Jens-Steffen Gutmann and Wolfgang Hatzack.
The CS Freiburg '99 Team.
In
Third International Workshop on RoboCup.
1999.
(Show abstract)
(Hide abstract)
(PS.GZ)
Based on the design of the CS Freiburg team, which participated successfully
in Robocup'98, we developed a new team of robotic soccer players. While the
hardware components and software architecture remained mainly unchanged, we
invested some effort to improve the sensor data gathering and
interpretation, the tactical components and the behavior-based control
module. The main goal is to enable the players to act in a truly cooperative
style which leads, for instance, to passing the ball from one player to
another.
-
Jens-Steffen Gutmann, Wolfram Burgard, Dieter Fox and Kurt Konolige.
An Experimental Comparison of Localization Methods.
In
International Conference on Intelligent Robots and
Systems (IROS 98).
Victoria, Canada 1998.
(PS.GZ)
-
Bernhard Nebel, Wolfgang Hatzack, Thilo Weigel, Jens-Steffen Gutmann, Immanuel Herrmann, Frank Rittinger and Augustinus Topor.
CS Freiburg's Participation at RoboCup'98: The World
Champions in Robotic Soccer.
AI Communications 11, pp. 243-248. 1998.
(Show abstract)
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(PS.GZ)
Robotic soccer is a challenging research domain that can be used to
explore new problems and to demonstrate new techniques. We
participated in RoboCup'98 in order to explore the problems of
cooperation in multi-robot-systems and to demonstrate our
self-localization techniques based on laser range finders. In this
paper we sketch the main technical points of our team, give a
description of the process of developing our team before and during
the competition, and describe how we viewed the competition in general.
-
Sebastian Thrun, Jens-Steffen Gutmann, Dieter Fox, Wolfram Burgard and Benjamin J. Kuipers.
Integrating Topological and Metric Maps for Mobile Robot
Navigation: A Statistical Approach.
In
Proceedings of the 15th National Conference on Artificial
Intelligence (AAAI-98).
1998.
(PS.GZ)
-
Jens-Steffen Gutmann and Bernhard Nebel.
Navigation mobiler Roboter mit Laserscans.
In
Autonome Mobile Systeme 1997 (AMS'97), pp. 36-47.
Springer-Verlag 1997.
(Show abstract)
(Hide abstract)
(PS.GZ)
Es wird ein Verfahren zur Erstellung einer topologischen Karte aus
Laserscandaten für die Navigation mobiler Roboter beschrieben. Aus
einem Satz sich korrekt überdeckender 360 Grad Scans wird ein
Sichtbarkeitsgraph erstellt, wobei Knoten Scanpositionen und Kanten
die relative Anzahl gemeinsamer Scanpunkte (genannt Sichtbarkeit)
repräsentieren. Aus der Sichtbarkeit und der Distanz der
Scanpositionen wird eine subjektive Wahrscheinlichkeit für die
Befahrbarkeit zwischen den Scanpositionen berechnet. Durch Annahme
von Unabhängigkeit der berechneten Wahrscheinlichkeiten wird mittels
uniformer Kostensuche ein möglichst kurzer und sicher befahrbarer Pfad
bestimmt. Das Verfahren wurde auf einem Pioneer-1-Roboter mit
SICK-Laserscanner implementiert und erprobt. Für die Navigation zu
jedem Zwischenziel entlang des Pfades wurde ein gitterbasierter
lokaler Wegeplaner verwendet. Dadurch konnte ein hoher Grad an
Robustheit erlangt werden. Das System ist in der Lage
unvorhergesehenen Hindernissen auszuweichen, nicht passierbare Wege zu
erkennen und alternative Wege zu finden.
-
Jens-Steffen Gutmann and Christian Schlegel.
AMOS: Comparison of Scan Matching Approaches for
Self-Localization in Indoor Environments.
In
Proceedings of the First Euromicro Workshop on Advanced
Mobile Robots (EUROBOT '96), pp. 61-68.
1996.
(PS.GZ)
-
Alper Aydemir, Moritz Göbelbecker, Andrzej Pronobis, Kristoffer Sjöö and Patric Jensfelt.
Plan-based Object Search and Exploration Using Semantic Spatial Knowledge in the Real World.
In
Proceedings of the 5th European Conference on Mobile Robotics (ECMR 2011).
2011.
To appear.
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(PDF)
(BIB)
In this paper we present a principled planner based
approach to the active visual object search problem in unknown
environments. We make use of a hierarchical planner that combines
the strength of decision theory and heuristics. Furthermore, our
object search approach leverages on the conceptual spatial
knowledge in the form of object cooccurences and semantic place
categorisation. A hierarchical model for representing object
locations is presented with which the planner is able to perform
indirect search. Finally we present real world experiments to show
the feasibility of the approach.
-
Moritz Göbelbecker, Charles Gretton and Richard W. Dearden.
A Switching Planner for Combined Task and Observation Planning.
In
Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI 2011).
2011.
(Show abstract)
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(PDF)
(BIB)
From an automated planning perspective the problem of
practical mobile robot control in realistic environments poses many
important and contrary challenges. On the one hand, the planning
process must be lightweight, robust, and timely. Over the lifetime of
the robot it must always respond quickly with new plans that
accommodate exogenous events, changing objectives, and the underlying
unpredictability of the environment. On the other hand, in order to
promote efficient behaviours the planning process must perform
computationally expensive reasoning about contingencies and possible
revisions of subjective beliefs according to quantitatively modelled
uncertainty in acting and sensing. Towards addressing these
challenges, we develop a continual planning approach that switches
between using a fast satisficing ``classical'' planner, to decide on
the overall strategy, and decision-theoretic planning to solve small
abstract subproblems where deeper consideration of the sensing model
is both practical, and can significantly impact overall
performance. We evaluate our approach in large problems from a
realistic robot exploration domain.
-
Moritz Göbelbecker, Alper Aydemir, Andrzej Pronobis, Kristoffer Sjöö and Patric Jensfelt.
A Planning Approach to Active Visual Search in Large Environments.
In
Proceedings of the AAAI-11 Workshop on Automated Action Planning for Autonomous Mobile Robots (PAMR).
2011.
Workshop version of the ECMR11 paper "Plan-based Object Search and Exploration Using Semantic Spatial Knowledge in the Real World".
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
In this paper we present a principled planner based
approach to the active visual object search problem in unknown
environments. We make use of a hierarchical planner that combines
the strength of decision theory and heuristics. Furthermore, our
object search approach leverages on the conceptual spatial
knowledge in the form of object co-occurrences and semantic place
categorisation. A hierarchical model for representing object
locations is presented with which the planner is able to perform
indirect search. Finally we present real world experiments to show
the feasibility of the approach.
-
Moritz Göbelbecker, Charles Gretton and Richard W. Dearden.
A Switching Planner for Combined Task and Observation Planning.
In
Electronic Proceedings of the Workshop on Decision Making in Partially Observable, Uncertain Worlds: Exploring Insights from Multiple Communities at the Twenty-Second International Join Conference on Artificial Intelligence (DMPOUW 2011).
2011.
Workshop version of the AAAI11 paper of the same title..
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
From an automated planning perspective the problem of
practical mobile robot control in realistic environments poses many
important and contrary challenges. On the one hand, the planning
process must be lightweight, robust, and timely. Over the lifetime of
the robot it must always respond quickly with new plans that
accommodate exogenous events, changing objectives, and the underlying
unpredictability of the environment. On the other hand, in order to
promote efficient behaviours the planning process must perform
computationally expensive reasoning about contingencies and possible
revisions of subjective beliefs according to quantitatively modelled
uncertainty in acting and sensing. Towards addressing these
challenges, we develop a continual planning approach that switches
between using a fast satisficing ``classical'' planner, to decide on
the overall strategy, and decision-theoretic planning to solve small
abstract subproblems where deeper consideration of the sensing model
is both practical, and can significantly impact overall
performance. We evaluate our approach in large problems from a
realistic robot exploration domain.
-
Marc Hanheide, Charles Gretton, Richard Dearden, Nick Hawes, Jeremy Wyatt, Andrzej Pronobis, Alper Aydemir, Moritz Göbelbecker and Hendrik Zender.
Exploiting Probabilistic Knowledge under Uncertain Sensing for Efficient Robot Behaviour.
In
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011).
2011.
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(PDF)
(BIB)
Robots must perform tasks efficiently and reliably
while acting under uncertainty. One way to achieve efficiency is to
give the robot common-sense knowledge about the structure of the
world. Reliable robot behaviour can be achieved by modelling the
uncertainty in the world probabilistically. We present a robot system
that combines these two approaches and demonstrate the improvements in
efficiency and reliability that result. Our first contribution is a
probabilistic relational model integrating common-sense knowledge
about the world in general, with observations of a particular
environment. Our second contribution is a continual planning system
which is able to plan in the large problems posed by that model, by
automatically switching between decision-theoretic and classical
procedures. We evaluate our system on object search tasks in two
different real-world indoor environments. By reasoning about the
trade-offs between possible courses of action with different
informational effects, and exploiting the cues and general structures
of those environments, our robot is able to consistently demonstrate
efficient and reliable goal-directed behaviour.
-
D. Skočaj, M. Kristan, A. Leonardis, M. Mahnič, A. Vrečko, M. Janíček, G.-J. M. Kruijff, P. Lison, M. Zillich, C. Gretton, M. Hanheide and Moritz Göbelbecker.
A system approach to interactive learning of visual concepts.
In
Tenth International Conference on Epigenetic Robotics (EPIROB 2010).
2010.
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(PDF)
(BIB)
In this work we present a system and underlying
mechanisms for continuous learning of visual concepts in dialogue
with a human.
-
Marc Hanheide, Nick Hawes, Jeremy Wyatt, Moritz Göbelbecker, Michael Brenner, Kristoffer Sjöö, Alper Aydemir, Patric Jensfelt, Hendrik Zender and Geert-Jan Kruijff.
A Framework for Goal Generation and Management.
In
Proceedings of the AAAI Workshop on Goal-Directed Autonomy.
2010.
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(BIB)
Goal-directed behaviour is often viewed as an
essential char- acteristic of an intelligent system, but
mechanisms to generate and manage goals are often overlooked. This
paper addresses this by presenting a framework for autonomous goal
gener- ation and selection. The framework has been implemented as
part of an intelligent mobile robot capable of exploring unknown
space and determining the category of rooms au- tonomously. We
demonstrate the efficacy of our approach by comparing the
performance of two versions of our inte- grated system: one with
the framework, the other without. This investigation leads us
conclude that such a framework is desirable for an integrated
intelligent system because it re- duces the complexity of the
problems that must be solved by other behaviour-generation
mechanisms, it makes goal- directed behaviour more robust in the
face of a dynamic and unpredictable environments, and it provides
an entry point for domain-specific knowledge in a more general
system.
-
Moritz Göbelbecker, Thomas Keller, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
Coming Up with Good Excuses: What To Do When No Plan Can be Found.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 81-88.
AAAI Press 2010.
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(PDF)
(BIB)
When using a planner-based agent architecture, many things can
go wrong. First and foremost, an agent might fail to execute
one of the planned actions for some reasons. Even more
annoying, however, is a situation where the agent is
incompetent, i.e., unable to come up with a plan. This
might be due to the fact that there are principal reasons that
prohibit a successful plan or simply because the task's
description is incomplete or incorrect. In either case, an
explanation for such a failure would be very helpful. We will
address this problem and provide a formalization of coming
up with excuses for not being able to find a plan. Based
on that, we will present an algorithm that is able to find
excuses and demonstrate that such excuses can be found in
practical settings in reasonable time.
-
Nick Hawes, Marc Hanheide, Kristoffer Sjöö, Alper Aydemir, Patric Jensfelt, Moritz Göbelbecker, Michael Brenner, Hendrik Zender, Pierre Lison, Ivana Kruijff-Korbayov, Geert-Jan M. Kruijff and Michael Zillich.
Dora The Explorer: A Motivated Robot.
In
Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010).
2010.
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Dora the Explorer is a mobile robot with a sense of
curios- ity and a drive to explore its world. Given an incomplete
tour of an indoor environment, Dora is driven by internal
motivations to probe the gaps in her spatial knowledge. She
actively explores regions of space which she hasn't previously
visited but which she expects will lead her to further unex-
plored space. She will also attempt to determine the cate- gories
of rooms through active visual search for functionally important
objects, and through ontology-driven inference on the results of
this search.
-
Moritz Göbelbecker and Christian Dornhege.
Realistic Cities in Simulated Environments - An Open Street Map to Robocup Rescue Converter.
In
Online-Proceedings of the Fourth International Workshop on Synthetic Simulation
and Robotics to Mitigate Earthquake Disaster (SRMED 2009).
2009.
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(PDF)
A general problem when developing large scale disaster simulation environments is to acquire GIS data.
In this work, we tackle the problem of map generation from public sources.
Usually the major problem is not only the data conversion itself, but to get access to the data at all.
We solve this problem by using the website OpenStreetMap.org, that provides mapping data for the whole world in a wiki-style concept, as our source of data,
thus being able to generate maps for almost any city.
The data is converted to the format required by the Robocup Rescue Simulation System, enabling simulations
on various real-world scenarios.
-
Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger, Joerg Stueckler and Bernhard Nebel.
Successful Search and Rescue in Simulated Disaster Areas.
In
Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
(Show abstract)
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(PDF)
RoboCupRescue Simulation is a large-scale multi-agent simulation
of urban disasters where, in order to save lives and minimize damage, rescue
teams must effectively cooperate despite sensing and communication limitations.
This paper presents the comprehensive search and rescue approach of the ResQ
Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup
2004.
Specific contributions include the predictions of travel costs and civilian lifetime,
the efficient coordination of an active disaster space exploration, as well as
an any-time rescue sequence optimization based on a genetic algorithm.
We compare the performances of our team and others in terms of their capability
of extinguishing fires, freeing roads from debris, disaster space exploration, and
civilian rescue. The evaluation is carried out with information extracted from
simulation log files gathered during RoboCup 2004. Our results clearly explain
the success of our team, and also confirm the scientific approaches proposed in
this paper.
-
Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger and Joerg Stueckler.
ResQ Freiburg: Team Description and Evaluation, Team Description Paper, Rescue Simulation League.
In
CDROM Proceedings of the International RoboCup Symposium '04.
Lisbon, Portugal 2004.
(PDF)
-
Raz Nissim, Jörg Hoffmann and Malte Helmert.
Computing Perfect Heuristics in Polynomial Time:
On Bisimulation and Merge-and-Shrink Abstractions in Optimal
Planning.
In
Proceedings of the
Twenty-Second
International Joint Conference on Artificial Intelligence
(IJCAI 2011), pp. 1983-1990.
2011.
Erratum: In Section 7, we introduce greedy bisimulation
as only respecting the bisimulation property for transitions
(s, l, s') where sd(s) <= sd(s'). The implementation
we evaluate in Section 8 is actually even more greedy than that,
only respecting transitions where sd(s) < sd(s').
Using the definition from Section 7 leads to a strategy that
behaves very similarly to the strategies using regular (non-greedy)
bisimulation on these benchmarks..
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A* with admissible heuristics is a very successful approach to
optimal planning. But how to derive such heuristics
automatically? Merge-and-shrink abstraction (M&S) is a
general approach to heuristic design whose key advantage is
its capability to make very fine-grained choices in defining
abstractions. However, little is known about how to actually
make these choices. We address this via the well-known notion
of bisimulation. When aggregating only bisimilar
states, M&s yields a perfect heuristic. Alas,
bisimulations are exponentially large even in trivial
domains. We show how to apply label reduction -- not
distinguishing between certain groups of operators -- without
incurring any information loss, while potentially reducing
bisimulation size exponentially. In several benchmark domains,
the resulting algorithm computes perfect heuristics in
polynomial time. Empirically, we show that approximating
variants of this algorithm improve the state of the art in
M&S heuristics. In particular, a hybrid of two such
variants is competitive with the leading heuristic LM-cut.
-
Carmel Domshlak, Malte Helmert, Erez Karpas, Emil Keyder, Silvia Richter, Gabriele Röger, Jendrik Seipp and Matthias Westphal.
BJOLP: The Big Joint Optimal Landmarks Planner
(planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Deterministic Part, pp. 91-95.
2011.
(Show abstract)
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(PDF)
BJOLP, The Big Joint Optimal Landmarks Planner uses landmarks
to derive an admissible heuristic, which is then used to guide
a search for a cost-optimal plan. In this paper we review
landmarks and describe how they can be used to derive an
admissible heuristic. We conclude with presenting the BJOLP
planner.
-
Silvia Richter, Matthias Westphal and Malte Helmert.
LAMA 2008 and 2011 (planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Deterministic Part, pp. 50-54.
2011.
(Show abstract)
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(PDF)
LAMA is a propositional planning system based on heuristic
search with landmarks. This paper describes two versions of
LAMA that were entered into the 2011 International Planning
Competition: the original LAMA as developed for the 2008
competition and a new re-implementation of LAMA that uses the
latest version of the Fast Downward Planning Framework.
Landmarks are propositions that must be true in every solution
of a planning task. LAMA uses a heuristic derived from
landmarks in conjunction with the well-known FF
heuristic. LAMA builds on the Fast Downward Planning System
using non-binary (but finite domain) state variables and
multi-heuristic search. A weighted A* search is used with
iteratively decreasing weights, so that the planner continues
to search for plans of better quality until the search is
terminated. LAMA combines cost-to-goal and distance-to-goal
estimates with the aim of finding good solutions using
reasonable runtime.
-
Malte Helmert and Carmel Domshlak.
LM-Cut: Optimal Planning with the Landmark-Cut Heuristic
(planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Deterministic Part, pp. 103-105.
2011.
(Show abstract)
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(PDF)
The LM-Cut planner uses the landmark-cut heuristic, introduced
by the authors in 2009, within a standard A* progression
search framework to find optimal sequential plans for
STRIPS-style planning tasks. This short paper recapitulates
the main ideas surrounding the landmark-cut heuristic and
provides pointers for further reading.
-
Raz Nissim, Jörg Hoffmann and Malte Helmert.
The Merge-and-Shrink Planner: Bisimulation-based
Abstraction for Optimal Planning (planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Deterministic Part, pp. 106-107.
2011.
(Show abstract)
(Hide abstract)
(PDF)
Merge-and-shrink abstraction is a general approach to
heuristic design whose key advantage is its capability to make
very fine-grained choices in defining abstractions. The
Merge-and-shrink planner uses two different strategies for
making these choices, both based on the well-known notion of
bisimulation. The resulting heuristics are used in two
sequential runs of A* search.
-
Carmel Domshlak, Malte Helmert, Erez Karpas and Shaul Markovitch.
The SelMax Planner: Online Learning for Speeding up Optimal
Planning (planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Deterministic Part, pp. 108-112.
2011.
(Show abstract)
(Hide abstract)
(PDF)
The SelMax planner combines two state-of-the-art admissible
heuristics using an online learning approach. In this paper we
describe the online learning approach employed by SelMax,
briefly review the Fast Downward framework, and describe the
SelMax planner.
-
Malte Helmert, Gabriele Röger, Jendrik Seipp, Erez Karpas, Jörg Hoffmann, Emil Keyder, Raz Nissim, Silvia Richter and Matthias Westphal.
Fast Downward Stone Soup (planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Deterministic Part, pp. 38-45.
2011.
(Show abstract)
(Hide abstract)
(PDF)
Fast Downward Stone Soup is a sequential portfolio planner
that uses various heuristics and search algorithms that have
been implemented in the Fast Downward planning system.
We present a simple general method for concocting "planner
soups", sequential portfolios of planning algorithms, and
describe the actual recipes used for Fast Downward Stone Soup
in the sequential optimization and sequential satisficing
tracks of IPC 2011.
-
Chris Fawcett, Malte Helmert, Holger Hoos, Erez Karpas, Gabriele Röger and Jendrik Seipp.
FD-Autotune: Automated Configuration of Fast Downward
(planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Deterministic Part, pp. 31-37.
2011.
(Show abstract)
(Hide abstract)
(PDF)
The FD-Autotune submissions for the IPC-2011 sequential tracks
consist of three instantiations of the latest, highly
parametric version of the Fast Downward Planning
Framework. These instantiations have been automatically
configured for performance on a wide range of planning
domains, using the well-known ParamILS configurator. Two of
the instantiations were entered into the sequential
satisficing track and one into the sequential optimising
track. We describe how the extremely large configuration space
of Fast Downward was restricted to a subspace that, although
still very large, can be managed by state-of-the-art automated
configuration procedures, and how ParamILS was then used to
obtain performance-optimised configurations.
-
Chris Fawcett, Malte Helmert, Holger Hoos, Erez Karpas, Gabriele Röger and Jendrik Seipp.
FD-Autotune: Domain-Specific Configuration using Fast Downward
(planner abstract).
In
Seventh
International Planning Competition (IPC 2011), Planning and
Learning Part.
2011.
(Show abstract)
(Hide abstract)
(PDF)
The FD-Autotune learning planning system is based on the idea
of domain-specific configuration of the latest, highly
parametric version of the Fast Downward Planning Framework by
means of a generic automated algorithm configuration
procedure. We describe how the extremely large configuration
space of Fast Downward was restricted to a subspace that,
although still very large, can be managed by state-of-the-art
automated configuration procedures. FD-Autotune uses the
well-known ParamILS configurator and was realised using the
recently developed HAL experimentation environment.
-
Malte Helmert, Gabriele Röger and Erez Karpas.
Fast Downward Stone Soup: A Baseline for Building Planner Portfolios.
In
Proceedings of the ICAPS-2011
Workshop on Planning and Learning (PAL), pp. 28-35.
2011.
(Show abstract)
(Hide abstract)
(PDF)
Fast Downward Stone Soup is a sequential portfolio planner that
uses various heuristics and search algorithms that
have been implemented in the Fast Downward planning system.
We present a simple general method for concocting "planner
soups", sequential portfolios of planning algorithms, and
describe the actual recipes used for Fast Downward Stone Soup
in the sequential optimization and sequential satisficing
tracks of IPC 2011.
This paper is, first and foremost, a planner description.
Fast Downward Stone Soup was entered into the sequential
(non-learning) tracks of IPC 2011. Due to time constraints, we
did not enter it into the learning competition at IPC
2011. However, we believe that the approach might still be of
interest to the planning and learning community, as it
represents a baseline against which other, more sophisticated
portfolio learners can be usefully compared.
-
Chris Fawcett, Malte Helmert, Holger Hoos, Erez Karpas, Gabriele Röger and Jendrik Seipp.
FD-Autotune: Domain-Specific Configuration using Fast Downward.
In
Proceedings of the ICAPS-2011
Workshop on Planning and Learning (PAL), pp. 13-20.
2011.
(Show abstract)
(Hide abstract)
(PDF)
In this work, we present the FD-Autotune learning planning
system, which is based on the idea of domain-specific
configuration of the latest, highly parametric version of the
Fast Downward Planning Framework by means of a generic
automated algorithm configuration procedure. We describe how
the extremely large configuration space of Fast Downward was
restricted to a subspace that, although still very large, can
be managed by a state-of-the-art automated configuration
procedure. Additionally, we give preliminary results obtained
from applying our approach to the nine domains of the IPC-2011
learning track, using the well-known ParamILS configurator
and the recently developed HAL experimentation environment.
-
Raz Nissim, Jörg Hoffmann and Malte Helmert.
Computing Perfect Heuristics in Polynomial Time:
On Bisimulation and Merge-and-Shrink Abstractions in Optimal
Planning.
In
Proceedings of the ICAPS-2011
Workshop on Heuristics for Domain-independent Planning (HDIP), pp. 5-13.
2011.
Superseded by the IJCAI 2011 paper by the same name.
(Show abstract)
(Hide abstract)
(PDF)
A* with admissible heuristics is a very successful approach to
optimal planning. But how to derive such heuristics
automatically? Merge-and-shrink abstraction (M&S) is a
general approach to heuristic design whose key advantage is
its capability to make very fine-grained choices in defining
abstractions. However, little is known about how to actually
make these choices. We address this via the well-known notion
of bisimulation. When aggregating only bisimilar
states, M&s yields a perfect heuristic. Alas,
bisimulations are exponentially large even in trivial
domains. We show how to apply label reduction -- not
distinguishing between certain groups of operators -- without
incurring any information loss, while potentially reducing
bisimulation size exponentially. In several benchmark domains,
the resulting algorithm computes perfect heuristics in
polynomial time. Empirically, we show that approximating
variants of this algorithm improve the state of the art in
M&S heuristics. In particular, a hybrid of two such
variants is competitive with the leading heuristic LM-cut.
-
Jendrik Seipp and Malte Helmert.
Fluent Merging for Classical Planning Problems.
In
Proceedings of the ICAPS-2011
Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), pp. 47-53.
2011.
Note: This version of the paper fixes two mistakes
(in Def. 2 and in the text after Def. 3) that are present in the
version of the paper that is linked from the workshop
webpage..
(Show abstract)
(Hide abstract)
(PDF)
Fluent merging is a reformulation technique for classical
planning problems that can be applied automatically or
semi-automatically. The reformulation strives to transform a
planning task into a representation that allows a planning
algorithm to find solutions more efficiently or to find
solutions of better quality. This work introduces different
approaches for fluent merging and evaluates them within a
state-of-the-art planning system.
-
Fahiem Bacchus, Carmel Domshlak, Stefan Edelkamp and Malte Helmert (eds.).
Proceedings of the
21st International
Conference on Automated Planning and Scheduling (ICAPS 2011).
AAAI Press, Menlo Park, California, USA 2011.
-
Malte Helmert and Gabriele Röger.
Relative-Order Abstractions for the Pancake Problem.
In
Helder Coelho, Rudi Studer and Michael Wooldridge (eds.),
Proceedings of the 19th European Conference on
Artificial Intelligence (ECAI
2010), pp. 745-750.
IOS Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
The pancake problem is a famous search problem where the
objective is to sort a sequence of objects (pancakes)
through a minimal number of prefix reversals
(flips). The best approaches for the problem are based
on heuristic search with abstraction (pattern database)
heuristics. We present a new class of abstractions for the
pancake problem called relative-order abstractions.
Relative-order abstractions have three advantages over the
object-location abstractions considered in previous
work. First, they are size-independent, i.e., do not
need to be tailored to a particular instance size of the
pancake problem. Second, they are more compact in that
they can represent a larger number of pancakes within
abstractions of bounded size. Finally, they can exploit
symmetries in the problem specification to allow
multiple heuristic lookups, significantly improving search
performance over a single lookup. Our experiments show that
compared to object-location abstractions, our new techniques
lead to an improvement of one order of magnitude in runtime
and up to three orders of magnitude in the number of generated
states.
-
Blai Bonet and Malte Helmert.
Strengthening Landmark Heuristics via Hitting Sets.
In
Helder Coelho, Rudi Studer and Michael Wooldridge (eds.),
Proceedings of the 19th European Conference on
Artificial Intelligence (ECAI
2010), pp. 329-334.
IOS Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
(technical report with proofs; PDF)
(slides of Blai's ECAI 2010 presentation; PDF)
(slides of Malte's SS 2010 group seminar presentation; PDF)
The landmark cut heuristic is perhaps the strongest known
polytime admissible approximation of the optimal delete
relaxation heuristic h+. Equipped with
this heuristic, a best-first search was able to optimally
solve 40% more benchmark problems than the winners of the
sequential optimization track of IPC 2008. We show that this
heuristic can be understood as a simple relaxation of a
hitting set problem, and that stronger heuristics can be
obtained by considering stronger relaxations. Based on
these findings, we propose a simple polytime method for
obtaining heuristics stronger than landmark cut, and
evaluate them over benchmark problems. We also show that
hitting sets can be used to characterize
h+ and thus provide a fresh and novel
insight for better comprehension of the delete relaxation.
-
Emil Keyder, Silvia Richter and Malte Helmert.
Sound and Complete Landmarks for And/Or Graphs.
In
Helder Coelho, Rudi Studer and Michael Wooldridge (eds.),
Proceedings of the 19th European Conference on
Artificial Intelligence (ECAI
2010), pp. 335-340.
IOS Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
Landmarks for a planning problem are subgoals that are
necessarily made true at some point in the execution of any
plan. Since verifying that a fact is a landmark is
PSPACE-complete, earlier approaches have focused on finding
landmarks for the delete relaxation Π+.
Furthermore, some of these approaches have approximated
this set of landmarks, although it has been shown that the
complete set of causal delete-relaxation landmarks can
be identified in polynomial time by a simple procedure over
the relaxed planning graph. Here, we give a declarative
characterisation of this set of landmarks and show that the
procedure computes the landmarks described by our
characterisation. Building on this, we observe that the
procedure can be applied to any delete-relaxation problem and
take advantage of a recent compilation of the
m-relaxation of a problem into a problem with no delete
effects to extract landmarks that take into account delete
effects in the original problem. We demonstrate that this
approach finds strictly more causal landmarks than previous
approaches and discuss the relationship between increased
computational effort and experimental performance, using these
landmarks in a recently proposed admissible landmark-counting
heuristic.
-
Malte Helmert.
Lessons Learned from Benchmarking in the Automated Planning
Community.
In
Proceedings of the
ECAI 2010
Workshop on Benchmarking Intelligent (Multi-) Robot Systems.
2010.
(PDF)
-
Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem.
In
Maria Fox and David Poole (eds.),
Proceedings of the Twenty-Fourth AAAI Conference on Artificial
Intelligence (AAAI
2010), pp. 51-58.
AAAI Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
We consider the stochastic variant of the Canadian
Traveler's Problem, a path planning problem where adverse
weather can cause some roads to be untraversable. The agent
does not initially know which roads can be used. However, it
knows a probability distribution for the weather, and it can
observe the status of roads incident to its location. The
objective is to find a policy with low expected travel cost.
We introduce and compare several algorithms for the
stochastic CTP. Unlike the optimistic approach most
commonly considered in the literature, the new approaches we
propose take uncertainty into account explicitly. We show
that this property enables them to generate policies of much
higher quality than the optimistic one, both theoretically
and experimentally.
-
Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem
(Extended Abstract).
In
Ariel Felner and Nathan Sturtevant (eds.),
Proceedings of the Third Annual Symposium on Combinatorial
Search (SoCS 2010), pp. 147-148.
AAAI Press 2010.
Extended abstract of the AAAI paper by the same name.
(PDF)
-
Malte Helmert.
Landmark Heuristics for the Pancake Problem.
In
Ariel Felner and Nathan Sturtevant (eds.),
Proceedings of the Third Annual Symposium on Combinatorial
Search (SoCS 2010), pp. 109-110.
AAAI Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
We describe the gap heuristic for the pancake problem,
which dramatically outperforms current abstraction-based
heuristics for this problem. The gap heuristic belongs to a
family of landmark heuristics that have recently been
very successfully applied to planning problems.
-
Malte Helmert and Hauke Lasinger.
The Scanalyzer Domain: Greenhouse Logistics as a Planning Problem.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 234-237.
AAAI Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
We introduce the Scanalyzer planning domain, a domain for
classical planning which models the problem of automatic
greenhouse logistic management.
At its mathematical core, the Scanalyzer domain is a
permutation problem with striking similarities to common
search benchmarks such as Rubik's Cube or TopSpin. At the same
time, it is also a real application domain, and efficient
algorithms for the problem are of considerable practical
interest.
The Scanalyzer domain was used as a benchmark for sequential
planners at the last International Planning Competition. The
competition results show that domain-independent automated
planners can find solutions of comparable quality to those
generated by specialized algorithms developed by domain
experts, while being considerably more flexible.
-
Robert Mattmüller, Manuela Ortlieb, Malte Helmert and Pascal Bercher.
Pattern Database Heuristics for Fully Observable Nondeterministic Planning.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 105-112.
AAAI Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
(slides; PDF)
(BIB)
When planning in an uncertain environment, one is often
interested in finding a contingent plan that prescribes
appropriate actions for all possible states that may be
encountered during the execution of the plan. We consider the
problem of finding strong and strong cyclic plans for fully
observable nondeterministic (FOND) planning problems. The
algorithm we choose is LAO*, an informed explicit state search
algorithm. We investigate the use of pattern database (PDB)
heuristics to guide LAO* towards goal states. To obtain a
fully domain-independent planning system, we use an automatic
pattern selection procedure that performs local search in the
space of pattern collections. The evaluation of our system on
the FOND benchmarks of the Uncertainty Part of the
International Planning Competition 2008 shows that our
approach is competitive with symbolic regression search in
terms of problem coverage and speed.
-
Gabriele Röger and Malte Helmert.
The More, the Merrier: Combining Heuristic Estimators for
Satisficing Planning.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on
Automated Planning and Scheduling
(ICAPS 2010), pp. 246-249.
AAAI Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
(technical report; PDF)
We empirically examine several ways of exploiting the
information of multiple heuristics in a satisficing best-first
search algorithm, comparing their performance in terms of
coverage, plan quality, speed, and search guidance. Our
results indicate that using multiple heuristics for
satisficing search is indeed useful. Among the combination
methods we consider, the best results are obtained by the
alternation method of the "Fast Diagonally Downward"
planner.
-
Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem.
In
Proceedings of the
ICAPS-2010
Workshop on Planning and Scheduling Under Uncertainty.
2010.
Superseded by the AAAI 2010 paper by the same name.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
We consider the stochastic variant of the Canadian
Traveler's Problem, a path planning problem where adverse
weather can cause some roads to be untraversable. The agent
does not initially know which roads can be used. However, it
knows a probability distribution for the weather, and it can
observe the status of roads incident to its location. The
objective is to find a policy with low expected travel cost.
We introduce and compare several algorithms for the
stochastic CTP. Unlike the optimistic approach most
commonly considered in the literature, the new approaches we
propose take uncertainty into account explicitly. We show
that this property enables them to generate policies of much
higher quality than the optimistic one, both theoretically
and experimentally.
-
Dunbo Cai, Jörg Hoffmann and Malte Helmert.
Enhancing the Context-Enhanced Additive Heuristic with Precedence Constraints.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 50-57.
AAAI Press 2009.
(Show abstract)
(Hide abstract)
(PDF)
Recently, Helmert and Geffner proposed the context-enhanced
additive heuristic, where fact costs are evaluated relative to
context states that arise from achieving first a pivot
condition of each operator. As Helmert and Geffner pointed
out, the method can be generalized to consider contexts
arising from arbitrary precedence constraints over operator
conditions instead. Herein, we provide such a
generalization. We extend Helmert and Geffner's equations, and
discuss a number of design choices that arise. Drawing on
previous work on goal orderings, we design a family of methods
for automatically generating precedence constraints. We run
large-scale experiments, showing that the technique can help
significantly, depending on the choice of precedence
constraints. We shed some light on this by profiling the
behavior of all possible precedence constraints, using a
sampling technique.
-
Malte Helmert and Carmel Domshlak.
Landmarks, Critical Paths and Abstractions: What's the Difference Anyway?
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 162-169.
AAAI Press 2009.
(Show abstract)
(Hide abstract)
(PDF)
(Dagstuhl abstract; PDF)
Current heuristic estimators for classical domain-independent
planning are usually based on one of four ideas: delete
relaxations, critical paths, abstractions,
and, most recently, landmarks. Previously, these
different ideas for deriving heuristic functions were largely
unconnected.
We prove that admissible heuristics based on these ideas are
in fact very closely related. Exploiting this relationship, we
introduce a new admissible heuristic called the landmark
cut heuristic, which compares favourably with the state of
the art in terms of heuristic accuracy and overall
performance.
-
Silvia Richter and Malte Helmert.
Preferred Operators and Deferred Evaluation in Satisficing Planning.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 273-280.
AAAI Press 2009.
(Show abstract)
(Hide abstract)
(PDF)
Heuristic forward search is the dominant approach to
satisficing planning to date. Most successful planning
systems, however, go beyond plain heuristic search by
employing various search-enhancement techniques. One example
is the use of helpful actions or preferred operators,
providing information which may complement heuristic values.
A second example is deferred heuristic evaluation, a search
variant which can reduce the number of costly node
evaluations. Despite the wide-spread use of these
search-enhancement techniques however, we note that few
results have been published examining their usefulness. In
particular, while various ways of using, and possibly
combining, these techniques are conceivable, no work to date
has studied the performance of such variations. In this
paper, we address this gap by examining the use of preferred
operators and deferred evaluation in a variety of settings
within best-first search. In particular, our findings are
consistent with and help explain the good performance of the
winners of the satisficing tracks at IPC 2004 and 2008.
-
Christoph Betz and Malte Helmert.
Planning with h+ in Theory and Practice.
In
Proceedings of the
2nd
Workshop on Heuristics for Domain-independent Planning
at ICAPS 2009.
2009.
(Show abstract)
(Hide abstract)
(PDF)
Many heuristic estimators for classical planning are based on
the so-called delete relaxation, which ignores negative
effects of planning operators. Ideally, such heuristics would
compute the actual goal distance in the delete relaxation,
i.e., the cost of an optimal relaxed plan, denoted by
h+. However, current delete relaxation heuristics only provide
(often inadmissible) estimates to h+ because computing the
correct value is an NP-hard problem.
In this work, we consider the approach of planning with the
actual h+ heuristic from a theoretical and computational
perspective. In particular, we provide domain-dependent
complexity results that classify some standard benchmark
domains into ones where h+ can be computed efficiently and
ones where computing h+ is NP-hard. Moreover, we study
domain-dependent implementations of h+ which show that
the h+ heuristic provides very informative heuristic estimates
compared to other state-of-the-art heuristics.
-
Gabriele Röger and Malte Helmert.
Combining Heuristic Estimators for Satisficing Planning.
In
Proceedings of the
2nd
Workshop on Heuristics for Domain-independent Planning
at ICAPS 2009.
2009.
(Show abstract)
(Hide abstract)
(PDF)
The problem of effectively combining multiple heuristic
estimators has been studied extensively in the context of
optimal planning, but not in the context of satisficing
planning. To narrow this gap, we empirically examine several
ways of exploiting the information of multiple heuristics in a
satisficing best-first search algorithm, comparing their
performance in terms of coverage, plan quality and
runtime. Our empirical results indicate that using multiple
heuristics for satisficing search is indeed useful and that
the best results are not obtained by the most obvious
combination methods.
-
Christoph Betz and Malte Helmert.
Planning with h+ in Theory and Practice.
In
Bärbel Mertsching, Marcus Hund and Zaheer Aziz (eds.),
Proceedings of the 32nd Annual German Conference on Artificial
Intelligence (KI 2009), pp. 9-16.
Springer-Verlag 2009.
(Show abstract)
(Hide abstract)
(PDF)
Many heuristic estimators for classical planning are based on
the so-called delete relaxation, which ignores negative
effects of planning operators. Ideally, such heuristics would
compute the actual goal distance in the delete relaxation,
i.e., the cost of an optimal relaxed plan, denoted by
h+. However, current delete relaxation heuristics only provide
(often inadmissible) estimates to h+ because computing the
correct value is an NP-hard problem.
In this work, we consider the approach of planning with the
actual h+ heuristic from a theoretical and computational
perspective. In particular, we provide domain-dependent
complexity results that classify some standard benchmark
domains into ones where h+ can be computed efficiently and
ones where computing h+ is NP-hard. Moreover, we study
domain-dependent implementations of h+ which show that
the h+ heuristic provides very informative heuristic estimates
compared to other state-of-the-art heuristics.
-
Martin Wehrle and Malte Helmert.
The Causal Graph Revisited for Directed Model
Checking.
In
Jens Palsberg and Zhendong Su (eds.),
Proceedings of the 16th International Static Analysis Symposium
(SAS 2009), pp. 86-101.
Springer-Verlag 2009.
(Show abstract)
(Hide abstract)
(PDF)
(Dagstuhl abstract; PDF)
Directed model checking is a well-established technique to
tackle the state explosion problem when the aim is to find
error states in large systems. In this approach, the state
space traversal is guided through a function that estimates
the distance to nearest error states. States with lower
estimates are preferably expanded during the
search. Obviously, the challenge is to develop distance
functions that are efficiently computable on the one hand and
as informative as possible on the other hand. In this paper,
we introduce the causal graph structure to the context
of directed model checking. Based on causal graph analysis, we
first adapt a distance estimation function from AI planning to
directed model checking. Furthermore, we investigate an
abstraction that is guaranteed to preserve error states. The
experimental evaluation shows the practical potential of these
techniques.
-
Malte Helmert.
Research Statement: Heuristic Search for Domain-Independent
Planning.
In
2nd International Symposium on Combinatorial Search
(SoCS
2009).
2009.
(PDF)
-
Jörg Hoffmann, Piergiorgio Bertoli, Malte Helmert and Marco Pistore.
Message-Based Web Service Composition, Integrity
Constraints, and Planning under Uncertainty: A New
Connection.
Journal of Artificial Intelligence Research 35, pp. 49-117. 2009.
(Show abstract)
(Hide abstract)
(PDF)
Thanks to recent advances, AI Planning has become the
underlying technique for several applications. Figuring
prominently among these is automated Web Service Composition
(WSC) at the "capability" level, where services are
described in terms of preconditions and effects over
ontological concepts. A key issue in addressing WSC as
planning is that ontologies are not only formal vocabularies;
they also axiomatize the possible relationships between
concepts. Such axioms correspond to what has been termed
"integrity constraints" in the actions and change
literature, and applying a web service is essentially a belief
update operation. The reasoning required for belief update is
known to be harder than reasoning in the ontology itself. The
support for belief update is severely limited in current
planning tools.
Our first contribution consists in identifying an interesting
special case of WSC which is both significant and more
tractable. The special case, which we term forward
effects, is characterized by the fact that every
ramification of a web service application involves at least
one new constant generated as output by the web service. We
show that, in this setting, the reasoning required for belief
update simplifies to standard reasoning in the ontology
itself. This relates to, and extends, current notions of
"message-based" WSC, where the need for belief update is
removed by a strong (often implicit or informal) assumption of
"locality" of the individual messages. We clarify the
computational properties of the forward effects case, and
point out a strong relation to standard notions of planning
under uncertainty, suggesting that effective tools for the
latter can be successfully adapted to address the former.
Furthermore, we identify a significant sub-case, named
strictly forward effects, where an actual compilation
into planning under uncertainty exists. This enables us to
exploit off-the-shelf planning tools to solve message-based
WSC in a general form that involves powerful ontologies, and
requires reasoning about partial matches between concepts. We
provide empirical evidence that this approach may be quite
effective, using Conformant-FF as the underlying planner.
-
Malte Helmert.
Concise finite-domain representations for PDDL planning
tasks.
Artificial Intelligence 173, pp. 503-535. 2009.
(Show abstract)
(Hide abstract)
(PDF)
We introduce an efficient method for translating planning
tasks specified in the standard PDDL formalism into a concise
grounded representation that uses finite-domain state
variables instead of the straight-forward propositional
encoding.
Translation is performed in four stages. Firstly, we transform
the input task into an equivalent normal form expressed in a
restricted fragment of PDDL. Secondly, we synthesize
invariants of the planning task that identify groups of
mutually exclusive propositions which can be represented by a
single finite-domain variable. Thirdly, we perform an
efficient relaxed reachability analysis using logic
programming techniques to obtain a grounded representation of
the input. Finally, we combine the results of the third and
fourth stage to generate the final grounded finite-domain
representation.
The presented approach has originally been implemented as part
of the Fast Downward planning system for the 4th International
Planning Competition (IPC4). Since then, it has been used in a
number of other contexts with considerable success, and the
use of concise finite-domain representations has become a
common feature of state-of-the-art planners.
-
Gabriele Röger, Malte Helmert and Bernhard Nebel.
On the Relative Expressiveness of ADL and Golog: The Last
Piece in the Puzzle.
In
Proceedings of the Eleventh International Conference on
Principles of Knowledge Representation and Reasoning
(KR
2008), pp. 544-550.
AAAI Press 2008.
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Integrating agent programming languages and efficient action
planning is a promising approach because it combines the
expressive power of languages such as Golog with the possibility
of searching for plans efficiently. In order to integrate a
Golog interpreter with a planner, one has to understand,
however, which part of the expressiveness of Golog can be
captured by the planning language. Using Nebel's compilation
framework, we identify a maximal fragment of basic action
theories, the formalism Golog is based on, that is
expressively equivalent to the ADL subset of PDDL. As we will
show, almost all features that permit to specify incomplete
information in basic action theories cannot be compiled to ADL.
-
Malte Helmert and Héctor Geffner.
Unifying the Causal Graph and Additive Heuristics.
In
Proceedings of the 18th International Conference on Automated
Planning and Scheduling (ICAPS 2008), pp. 140-147.
AAAI Press 2008.
(Show abstract)
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Many current heuristics for domain-independent planning, such
as Bonet and Geffner's additive heuristic and Hoffmann
and Nebel's FF heuristic, are based on delete
relaxations. They estimate the goal distance of a search state
by approximating the solution cost in a relaxed task where
negative consequences of operator applications are
ignored. Helmert's causal graph heuristic, on the other
hand, approximates goal distances by solving a hierarchy of
"local" planning problems that only involve a single state
variable and the variables it depends on directly.
Superficially, the causal graph heuristic appears quite
unrelated to heuristics based on delete relaxation. In this
contribution, we show that the opposite is true. Using a
novel, declarative formulation of the causal graph heuristic,
we show that the causal graph heuristic is the additive
heuristic plus context. Unlike the original heuristic, our
formulation does not require the causal graph to be acyclic,
and thus leads to a proper generalization of both the causal
graph and additive heuristics. Empirical results show that the
new heuristic is significantly better informed than both
Helmert's original causal graph heuristic and the additive
heuristic and outperforms them across a wide range of standard
benchmarks.
-
Malte Helmert, Patrik Haslum and Jörg Hoffmann.
Explicit-State Abstraction: A New Method for Generating
Heuristic Functions.
In
Proceedings of the 23rd AAAI Conference on Artificial Intelligence
(AAAI
2008), pp. 1547-1550.
AAAI Press 2008.
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(slides; PDF)
Many AI problems can be recast as finding an optimal path in a
discrete state space. An abstraction defines an admissible
heuristic function as the distances in a smaller state space
where arbitrary sets of states are "aggregated" into single
states. A special case are pattern database (PDB) heuristics,
which aggregate states iff they agree on the state variables
inside the pattern. Explicit-state abstraction is more flexible,
explicitly aggregating selected pairs of states in a process
that interleaves composition of abstractions with abstraction of
the composites. The increased flexibility gains expressive
power: sometimes, the real cost function can be represented
concisely as an explicit-state abstraction, but not as a
PDB. Explicit-state abstraction has been applied to planning and
model checking, with highly promising empirical results.
-
Malte Helmert and Gabriele Röger.
How Good is Almost Perfect?
In
Proceedings of the 23rd AAAI Conference on Artificial Intelligence
(AAAI 2008), pp. 944-949.
AAAI Press 2008.
(Show abstract)
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(slides; PDF)
Heuristic search using algorithms such as A* and IDA* is the
prevalent method for obtaining optimal sequential solutions for
classical planning tasks. Theoretical analyses of these
classical search algorithms, such as the well-known results of
Pohl, Gaschnig and Pearl, suggest that such heuristic search
algorithms can obtain better than exponential scaling behaviour,
provided that the heuristics are accurate enough.
Here, we show that for a number of common planning benchmark
domains, including ones that admit optimal solution in
polynomial time, general search algorithms such as A* must
necessarily explore an exponential number of search
nodes even under the optimistic assumption of almost
perfect heuristic estimators, whose heuristic error is
bounded by a small additive constant.
Our results shed some light on the comparatively bad performance
of optimal heuristic search approaches in "simple" domains such
as GRIPPER. They suggest that in many domains, further
improvements in run-time require changes to other parts of the
planning algorithm than the heuristic estimator.
-
Malte Helmert and Robert Mattmüller.
Accuracy of Admissible Heuristic Functions in Selected Planning Domains.
In
Proceedings of the 23rd AAAI Conference on Artificial Intelligence
(AAAI 2008), pp. 938-943.
AAAI Press 2008.
(Show abstract)
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(PDF)
(slides; PDF)
(BIB)
The efficiency of optimal planning algorithms based on heuristic
search crucially depends on the accuracy of the heuristic
function used to guide the search. Often, we are interested in
domain-independent heuristics for planning. In order to assess the
limitations of domain-independent heuristic planning, it appears
interesting to analyse the (in)accuracy of common
domain-independent planning heuristics in the IPC benchmark
domains. For a selection of these domains, we analytically
investigate the accuracy of the h+
heuristic, the hm family of heuristics, and
certain (additive) pattern database heuristics, compared to the
perfect heuristic h*. Whereas
h+ and additive pattern database heuristics
usually return cost estimates proportional to the true cost,
non-additive hm and non-additive
pattern-database heuristics can yield results underestimating
the true cost by arbitrarily large factors.
-
Silvia Richter, Malte Helmert and Matthias Westphal.
Landmarks Revisited.
In
Proceedings of the 23rd AAAI Conference on Artificial Intelligence
(AAAI 2008), pp. 975-982.
AAAI Press 2008.
Note: After publication, we found a bug in our implementation
that affects the results in the columns "CG heuristic/local" and
"blind heuristic/local" of Table 1. The version of the paper available
for download here corrects these errors.
(Show abstract)
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(slides; PDF)
Landmarks for propositional planning tasks are variable
assignments that must occur at some point in every solution
plan. We propose a novel approach for using landmarks in
planning by deriving a pseudo-heuristic and combining it with
other heuristics in a search framework. The incorporation of
landmark information is shown to improve success rates and
solution qualities of a heuristic planner. We furthermore show
how additional landmarks and orderings can be found using the
information present in multi-valued state variable
representations of planning tasks. Compared to previously
published approaches, our landmark extraction algorithm provides
stronger guarantees of correctness for the generated landmark
orderings, and our novel use of landmarks during search solves
more planning tasks and delivers considerably better solutions.
-
Malte Helmert.
Understanding Planning Tasks: Domain Complexity and Heuristic
Decomposition.
Volume 4929 of Lecture Notes in Artificial Intelligence.
Springer-Verlag, Heidelberg 2008.
(Springer Online)
-
Malte Helmert, Patrik Haslum and Jörg Hoffmann.
Flexible Abstraction Heuristics for Optimal Sequential
Planning.
In
Proceedings of the Seventeenth International Conference
on Automated Planning and Scheduling (ICAPS 2007), pp. 176-183.
AAAI Press 2007.
(Show abstract)
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(PDF)
We describe an approach to deriving consistent heuristics for
automated planning, based on explicit search in abstract state
spaces. The key to managing complexity is interleaving
composition of abstractions over different sets of state
variables with abstraction of the partial composites.
The approach is very general and can be instantiated in many
different ways by following different abstraction
strategies. In particular, the technique subsumes
planning with pattern databases as a special case.
Moreover, with suitable abstraction strategies it is possible to
derive perfect heuristics in a number of classical benchmark
domains, thus allowing their optimal solution in polynomial
time.
To evaluate the practical usefulness of the approach, we perform
empirical experiments with one particular abstraction strategy.
Our results show that the approach is competitive with the state
of the art.
-
Malte Helmert and Gabriele Röger.
How Good is Almost Perfect?
In
Proceedings of the
ICAPS-2007
Workshop on Heuristics for Domain-independent Planning: Progress,
Ideas, Limitations, Challenges.
2007.
Superseded by the AAAI 2008 paper by the same name.
(Show abstract)
(Hide abstract)
(PDF)
Heuristic search using algorithms such as A* and IDA* is the
prevalent method for obtaining optimal sequential solutions for
classical planning tasks. Theoretical analyses of these
classical search algorithms, such as the well-known results of
Pohl, Gaschnig and Pearl, suggest that such heuristic search
algorithms can obtain better than exponential scaling behaviour,
provided that the heuristics are accurate enough.
Here, we show that for a number of common planning benchmark
domains, including ones that admit optimal solution in
polynomial time, general search algorithms such as A* must
necessarily explore an exponential number of search
nodes even under the optimistic assumption of almost
perfect heuristic estimators, whose heuristic error is
bounded by a small additive constant.
Our results shed some light on the comparatively bad performance
of optimal heuristic search approaches in "simple" domains such
as GRIPPER. They suggest that in many domains, further
improvements in run-time require changes to other parts of the
planning algorithm than the heuristic estimator.
-
Malte Helmert and Robert Mattmüller.
On the Accuracy of Admissible Heuristic Functions in
Selected Planning Domains.
In
Proceedings of the
ICAPS-2007
Workshop on Heuristics for Domain-independent Planning: Progress,
Ideas, Limitations, Challenges.
2007.
Superseded by the AAAI 2008 paper by the same name.
(Show abstract)
(Hide abstract)
(PDF)
The efficiency of optimal planning algorithms based on heuristic
search crucially depends on the accuracy of the heuristic
function used to guide the search. Often, we are interested in
domain-independent heuristics for planning. In assessing the
limitations of domain-independent heuristic planning, it appears
interesting to analyse the (in)accuracy of common
domain-independent planning heuristics in the IPC benchmark
domains. For a selection of these domains, we analytically
investigate the accuracy of the h+
heuristic, the hk family of heuristics, and
certain (additive) pattern database heuristics, compared to the
optimal heuristic h*. Whereas
h+ and additive pattern database heuristics
usually return cost estimates proportional to the true cost,
non-additive hk and non-additive
pattern-database heuristics can yield results underestimating
the true cost by arbitrarily large factors.
-
Silvia Richter, Malte Helmert and Charles Gretton.
A Stochastic Local Search Approach to Vertex Cover.
In
Proceedings of the 30th Annual German Conference on Artificial
Intelligence (KI 2007), pp. 412-426.
2007.
(Show abstract)
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(PDF)
We introduce a novel stochastic local search algorithm for the
vertex cover problem. Compared to current exhaustive search
techniques, our algorithm achieves excellent performance on a
suite of problems drawn from the field of biology. We also
evaluate our performance on the commonly used DIMACS benchmarks
for the related clique problem, finding that our approach is
competitive with the current best stochastic local search
algorithm for finding cliques. On three very large problem
instances, our algorithm establishes new records in solution
quality.
-
Patrik Haslum, Adi Botea, Malte Helmert, Blai Bonet and Sven Koenig.
Domain-Independent Construction of Pattern Database
Heuristics for Cost-Optimal Planning.
In
Proceedings of the 22nd AAAI Conference on Artificial
Intelligence (AAAI 2007), pp. 1007-1012.
AAAI Press 2007.
(Show abstract)
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(PDF)
Heuristic search is a leading approach to domain-independent
planning. For cost-optimal planning, however, existing
admissible heuristics are generally too weak to effectively
guide the search. Pattern database heuristics (PDBs), which are
based on abstractions of the search space, are currently one of
the most promising approaches to developing better admissible
heuristics. The informedness of PDB heuristics depends crucially
on the selection of appropriate abstractions (patterns).
Although PDBs have been applied to many search problems,
including planning, there are not many insights into how to
select good patterns, even manually. What constitutes a good
pattern depends on the problem domain, making the task even more
difficult for domain-independent planning, where the process
needs to be completely automatic and general. We present a novel
way of constructing good patterns automatically from the
specification of planning problem instances. We demonstrate that
this allows a domain-independent planner to solve planning
problems optimally in some very challenging domains, including a
STRIPS formulation of the Sokoban puzzle.
-
Malte Helmert, Robert Mattmüller and Sven Schewe.
Selective Approaches for Solving Weak Games.
In
Proceedings of the Fourth International Symposium on
Automated Technology for Verification and Analysis (ATVA 2006), pp. 200-214.
Springer-Verlag 2006.
(Show abstract)
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(PDF)
Model-checking alternating-time properties has recently
attracted much interest in the verification of distributed
protocols. While checking the validity of a specification in
alternating-time temporal logic (ATL) against an explicit
model is cheap (linear in the size of the formula and the
model), the problem becomes EXPTIME-hard when symbolic
models are considered. Practical ATL model-checking therefore
often consumes too much computation time to be tractable.
In this paper, we describe a novel approach for ATL
model-checking, which constructs an explicit weak model-checking
game on-the-fly. This game is then evaluated using heuristic
techniques inspired by efficient evaluation algorithms for
and/or-trees.
To show the feasibility of our approach, we compare its
performance to the ATL model-checking system MOCHA on some
practical examples. Using very limited heuristic guidance, we
achieve a significant speedup on these benchmarks.
-
Malte Helmert, Robert Mattmüller and Gabriele Röger.
Approximation Properties of Planning Benchmarks.
In
Proceedings of the 17th European Conference on Artificial
Intelligence (ECAI 2006), pp. 585-589.
2006.
(Show abstract)
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For many classical planning domains, the computational complexity of
non-optimal and optimal planning is known. However, little is known
about the area in between the two extremes of finding some plan
and finding optimal plans. In this contribution, we provide a
complete classification of the propositional domains from the first four
International Planning Competitions with respect to the approximation
classes PO, PTAS, APX, poly-APX, and NPO.
-
Malte Helmert.
New Complexity Results for Classical Planning Benchmarks.
In
Proceedings of the Sixteenth International Conference on Automated
Planning and Scheduling
(ICAPS 2006), pp. 52-61.
AAAI Press 2006.
(Show abstract)
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The 3rd and 4th International Planning Competitions have
enriched the set of benchmarks for classical propositional
planning by a number of novel and interesting planning domains.
Although they are commonly used for the purpose of evaluating
planner performance, the planning domains themselves have not
yet been studied in depth. In this contribution, we prove
complexity results for the decision problems related to finding
some plan, finding an optimal sequential
plan, and finding an optimal parallel plan in
these planning domains. Our results also provide some insights
into the question why planning is hard for some of the
domains under consideration.
-
Sebastian Kupferschmid and Malte Helmert.
A Skat Player Based on Monte Carlo Simulation.
In
H. Jaap van den Herik, Paolo Ciancarini and H. H. L. M. Donkers (eds.),
Proceedings of the Fifth International Conference on
Computer and Games (CG 2006), pp. 135-147.
Springer-Verlag 2006.
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We apply Monte Carlo simulation and alpha-beta search to the
card game of Skat, which is similar to Bridge, but
different enough to require some new algorithmic ideas besides
the techniques developed for Bridge. Our Skat-playing program
integrates well-known techniques such as move ordering
with two new search enhancements. Quasi-symmetry
reduction generalizes symmetry reductions, popularized by
Ginsberg's Partition Search algorithm, to search states which
are "almost equivalent". Adversarial heuristics
generalize ideas from single-agent search algorithms like A* to
two-player games, leading to guaranteed lower and upper bounds
for the score of a game position. Combining these techniques
with state-of-the-art tree search algorithms, our program
determines the game-theoretical value of a typical Skat hand
(with perfect information) in 10 milliseconds.
-
Malte Helmert.
The Fast Downward Planning System.
Journal of Artificial Intelligence Research 26, pp. 191-246. 2006.
(Show abstract)
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Fast Downward is a classical planning system based on heuristic
search. It can deal with general deterministic planning problems
encoded in the propositional fragment of PDDL2.2, including
advanced features like ADL conditions and effects and derived
predicates (axioms). Like other well-known planners such as HSP
and FF, Fast Downward is a progression planner, searching the
space of world states of a planning task in the forward
direction. However, unlike other PDDL planning systems, Fast
Downward does not use the propositional PDDL representation of a
planning task directly. Instead, the input is first translated
into an alternative representation called multi-valued
planning tasks, which makes many of the implicit constraints
of a propositional planning task explicit. Exploiting this
alternative representation, Fast Downward uses hierarchical
decompositions of planning tasks for computing its heuristic
function, called the causal graph heuristic, which is
very different from traditional HSP-like heuristics based on
ignoring negative interactionse of operators.
In this article, we give a full account of Fast Downward's
approach to solving multi-valued planning tasks. We extend our
earlier discussion of the causal graph heuristic to tasks
involving axioms and conditional effects and present some novel
techniques for search control that are used within Fast
Downward's best-first search algorithm: preferred
operators transfer the idea of helpful actions from local
search to global best-first search, deferred evaluation
of heuristic functions mitigates the negative effect of large
branching factors on search performance, and multi-heuristic
best-first search combines several heuristic evaluation
functions within a single search algorithm in an orthogonal way.
We also describe efficient data structures for fast state
expansion (successor generators and axiom
evaluators) and present a new non-heuristic search algorithm
called focused iterative-broadening search, which
utilizes the information encoded in causal graphs in a novel
way.
Fast Downward has proven remarkably successful: It won the
"classical" (i.e., propositional, non-optimising) track of the
4th International Planning Competition at ICAPS 2004, following
in the footsteps of planners such as FF and LPG. Our experiments
show that it also performs very well on the benchmarks of the
earlier planning competitions and provide some insights about
the usefulness of the new search enhancements.
-
Malte Helmert.
A Planning Heuristic Based on Causal Graph Analysis.
In
Proceedings of the Fourteenth International Conference on
Automated Planning and Scheduling
(ICAPS 2004), pp. 161-170.
AAAI Press 2004.
(Show abstract)
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(PDF)
In recent years, heuristic search methods for classical planning
have achieved remarkable results. Their most successful
representative, the FF algorithm, performs well over a wide
spectrum of planning domains and still sets the state of the art
for STRIPS planning. However, there are some planning domains in
which algorithms like FF and HSP perform poorly because their
relaxation method of ignoring the "delete lists" of
STRIPS operators loses too much vital information.
Planning domains which have many dead ends in the search space
are especially problematic in this regard. In some domains, dead
ends are readily found by the human observer yet remain
undetected by all propositional planning systems we are aware
of. We believe that this is partly because the STRIPS
representation obscures the important causal structure
of the domain, which is evident to humans.
In this paper, we propose translating STRIPS problems to a
planning formalism with multi-valued state variables in order to
expose this underlying causal structure. Moreover, we show how
this structure can be exploited by an algorithm for detecting
dead ends in the search space and by a planning heuristic based
on hierarchical problem decomposition.
Our experiments show excellent overall performance on the
benchmarks from the international planning competitions.
-
Malte Helmert.
Complexity results for standard benchmark domains in planning.
Artificial Intelligence 143 (2), pp. 219-262. 2003.
(Show abstract)
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The efficiency of AI planning systems is usually evaluated
empirically. For the validity of conclusions drawn from such
empirical data, the problem set used for evaluation is of
critical importance. In planning, this problem set usually, or
at least often, consists of tasks from the various planning
domains used in the first two international planning
competitions, hosted at the 1998 and 2000 AIPS conferences. It
is thus surprising that comparatively little is known about the
properties of these benchmark domains, with the exception of
BLOCKSWORLD, which has been studied extensively by
several research groups.
In this contribution, we try to remedy this fact by providing a
map of the computational complexity of non-optimal and optimal
planning for the set of domains used in the competitions. We
identify a common transportation theme shared by the
majority of the benchmarks and use this observation to define
and analyze a general transportation problem that generalizes
planning in several classical domains such as
LOGISTICS, MYSTERY and GRIPPER. We
then apply the results of that analysis to the actual
transportation domains from the competitions. We next examine
the remaining benchmarks, which do not exhibit a strong
transportation feature, namely SCHEDULE and
FREECELL.
Relating the results of our analysis to
empirical work on the behavior of the recently very successful
FF planning system, we observe that our theoretical
results coincide well with data obtained from empirical
investigations.
-
Malte Helmert.
Decidability and Undecidability Results for Planning with
Numerical State Variables.
In
M. Ghallab, J. Hertzberg and P. Traverso (eds.),
Proceedings of the Sixth International Conference on
Artificial Intelligence Planning and Scheduling
(AIPS 2002), pp. 303-312.
AAAI Press 2002.
(Show abstract)
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These days, propositional planning can be considered a quite
well-understood problem. Good algorithms are known that will
solve a wealth of very different and sometimes challenging
planning tasks, and theoretical computational properties of both
general STRIPS-style planning and the best-known benchmark
problems have been established.
However, propositional planning has a major drawback: The
formalism is too weak to allow for the easy encoding of many
genuinely interesting planning problems, specifically those
involving numbers. A recent effort to enhance the PDDL planning
language to cope with (among other additions) numerical state
variables, to be used at the third international planning
competition, has increased interest in these issues.
In this contribution, we analyze "STRIPS with numbers" from a
theoretical point of view. Specifically, we show that the
introduction of numerical state variables makes the planning
problem undecidable in the general case and many restrictions
thereof and identify special cases for which we can provide
decidability results.
-
Stefan Edelkamp and Malte Helmert.
The Model Checking Integrated Planning System (MIPS).
AI Magazine 22 (3), pp. 67-71. 2001.
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(PDF)
MIPS was the first general planning system based on model
checking methods. It can handle the STRIPS subset of the PDDL
language and some additional features from ADL, namely negative
preconditions and (universal) conditional effects. At the AIPS
2000 conference, MIPS was one of five planning systems to
be awarded for "Distinguished Performance" in the fully
automated track.
This short paper gives a brief introduction to BDDs and explains
the basic planning algorithm employed by MIPS, using a
simple logistics problem as an example.
-
Malte Helmert.
On the Complexity of Planning in Transportation Domains.
In
A. Cesta and D. Borrajo (eds.),
Proceedings of the 6th European Conference on Planning
(ECP 2001), pp. 349-360.
2001.
(Show abstract)
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(PDF)
The efficiency of AI planning systems is usually evaluated
empirically. Some benchmark problems are of particular
importance in this context, especially the ones used in the
competitions of the 1998 and 2000 AIPS conferences. Many of
these benchmarks share a common theme of transporting
portables, making use of mobiles traversing a
map of locations and roads.
In this contribution, we embed these benchmarks into a
well-structured hierarchy of transportation problems
and study the computational complexity of optimal and
non-optimal planning in this family of domains. We identify the
key domain features that make transportation tasks hard and try
to shed some light on the recent success of planning systems
based on heuristic local search, as observed in the AIPS 2000
competition.
-
Sylvie Thiebaux, Jörg Hoffmann and Bernhard Nebel.
In Defense of Axioms in PDDL.
Artificial Intelligence 168 (1-2), pp. 38-69. 2005.
(Show abstract)
(Hide abstract)
There is controversy as to whether explicit support for PDDL-like axioms and derived predicates
is needed for planners to handle real-world domains effectively. Many researchers
have deplored the lack of precise semantics for such axioms, while others have argued that
it might be best to compile them away. We propose an adequate semantics for PDDL axioms
and show that they are an essential feature by proving that it is impossible to compile
them away if we restrict the growth of plans and domain descriptions to be polynomial.
These results suggest that adding a reasonable implementation to handle axioms inside the
planner is beneficial for the performance. Our experiments confirm this suggestion.
-
Jörg Hoffmann and Sebastian Kupferschmid.
A Covering Problem for Hypercubes.
In
Leslie Pack Kaelbling and Alessandro Saffiotti (eds.),
Poster Proceedings of the 19th International Joint
Conference on Artificial Intelligence (IJCAI 2005), pp. 1523-1524.
2005.
(Show abstract)
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(PDF)
(PS.GZ)
(BIB)
We introduce a new NP-complete problem asking if a "query"
hypercube is (not) covered by a set of other "evidence"
hypercubes. This comes down to a form of constraint reasoning
asking for the satisfiability of a CNF formula where the
logical atoms are inequalities over single variables, with
possibly infinite variable domains. We empirically investigate
the location of the phase transition regions in two random
distributions of problem instances. We introduce a solution
method that iteratively constructs a representation of the
non-covered part of the query cube. In particular, the method
is not based on backtracking. Our experiments show that the
method is, in a significant range of instances, superior to the
backtracking method that results from translation to SAT, and
application of a state-of-the-art DP-based SAT solver.
-
Sebastian Trüg, Jörg Hoffmann and Bernhard Nebel.
Applying Automatic Planning Techniques to Airport
Ground-Traffic Control: A Feasibility Study.
In
S. Biundo, T. Frühwirth and G. Palm (eds.),
KI 2004: Advances in Artificial Intelligence.
Proceedings of the 27th Annual German Conference on Artificial
Intelligence, pp. 183-197.
Springer-Verlag 2004.
-
Jörg Hoffmann, Julie Porteous and Laura Sebastia.
Ordered Landmarks in Planning.
Journal of Artificial Intelligence Research 22, pp. 215-278. 2004.
(PS.GZ)
-
Ronen Brafman and Jörg Hoffmann.
Conformant Planning via Heuristic Forward Search: A New
Approach.
In
Proceedings of the Fourteenth International Conference on
Automated Planning and Scheduling (ICAPS 2004), pp. 355-364.
AAAI Press 2004.
(PS.GZ)
-
Bernd Becker, Markus Behle, Fritz Eisenbrand, Martin Fränzle, Marc Herbstritt, Christian Herde, Jörg Hoffmann, Daniel Kröning, Bernhard Nebel, Ilia Polian and Ralf Wimmer.
Bounded Model Checking and Inductive Verification of
Hybrid Discrete-continuous Systems.
In
Proceedings GI/ITG/GMM-Workshop Methoden und
Beschreibungssprachen zur Modellierung und Verifikation von
Schaltungen und Systemen, pp. 65-75.
Kaiserslautern 2004.
(Show abstract)
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We present a concept to signicantly advance the state of the art for bounded
model checking (BMC) and inductive verication (IV) of hybrid discrete-continuous
systems. Our approach combines the expertise of partners coming from dierent
domains, like hybrid systems modeling and digital circuit verication, bounded planning and heuristic search, combinatorial optimization and integer programming. After sketching the overall verication
ow we present rst results indicating that the
combination and tight integration of dierent verication engines is a rst step to
pave the way to fully automated BMC and IV of medium to large-scale networks of
hybrid automata.
-
Jörg Hoffmann.
Utilizing Problem Structure in Planning: A Local Search Approach.
Volume 2854 of Lecture Notes in Artificial Intelligence.
Springer-Verlag, Berlin, Heidelberg, New York 2003.
(Springer Online)
(extended abstract; PS.GZ)
-
Ronen Brafman and Jörg Hoffmann.
Conformant Planning via Heuristic Forward Search.
In
Proceedings of the Workshop on Planning under Uncertainty
and Incomplete Information at ICAPS'03.
Trento, Italy 2003.
(PS.GZ)
-
Stefan Edelkamp and Jörg Hoffmann.
Quo Vadis, IPC-4? - Proposals for the Classical Part of the
4th International Planning Competition.
In
Proceedings of the Workshop on the Competition at ICAPS'03.
Trento, Italy 2003.
(PS.GZ)
-
Jörg Hoffmann.
The Metric-FF Planning System: Translating "Ignoring
Delete Lists" to Numeric State Variables.
Journal of Artificial Intelligence Research Special issue on the 3rd International Planning Competition. 2003.
(PS.GZ)
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Jörg Hoffmann and Hector Geffner.
Branching Matters: Alternative Branching in Graphplan.
In
Proceedings of the Thirteenth International Conference on
Automated Planning and Scheduling.
Trento, Italy 2003.
(PS.GZ)
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Sylvie Thiebaux, Jörg Hoffmann and Bernhard Nebel.
In Defense of PDDL Axioms.
In
Proceedings of the 18th International Joint Conference on
Artificial Intelligence.
Acapulco, Mexico 2003.
(Show abstract)
(Hide abstract)
(PS.GZ)
There is controversy
as to whether explicit support for PDDL-like axioms and derived
predicates is needed for planners to handle real-world domains
effectively. Many researchers have deplored the lack of precise
semantics for such axioms, while others have argued that they are a
non-essential feature which is best compiled away. We propose an
adequate semantics for PDDL axioms and show that they are an essential
feature by proving that it is impossible to compile them away if we
restrict the growth of plans and domain descriptions to be polynomial.
These results suggest that adding a reasonable implementation to
handle axioms inside the planner is beneficial for the performance.
Our experiments confirm this suggestion.
-
Sylvie Thiebaux, Jörg Hoffmann and Bernhard Nebel.
In Defense of PDDL Axioms.
In
Proceedings of the Workshop on the Competition at ICAPS'03.
Trento, Italy 2003.
(PS.GZ)
-
Jörg Hoffmann.
Local Search Topology in Planning Benchmarks: A Theoretical Analysis.
In
M. Ghallab, J. Hertzberg and P. Traverso (eds.),
Proceedings of the Sixth International Conference on
Artificial Intelligence Planning and Scheduling (AIPS
2002).
AAAI Press 2002.
(PS.GZ)
(PDF)
-
Jörg Hoffmann.
Extending FF to Numerical State Variables.
In
Proceedings of the 15th European Conference on Artificial Intelligence.
Lyon, France 2002.
(PS.GZ)
-
Jörg Hoffmann and Bernhard Nebel.
RIFO revisited: Detecting Relaxed Irrelevance.
In
A. Cesta and D. Borrajo (eds.),
Proceedings of the 6th European Conference on Planning
(ECP 2001).
2001.
(Show abstract)
(Hide abstract)
(PS.GZ)
(PDF)
RIFO, as has been proposed by Nebel et al., is a method
that can automatically detect irrelevant information in planning tasks. The
idea is to remove such irrelevant information as a preprocess to planning.
While RIFO has been shown to be useful in a number of domains, its main
disadvantage is that it is not completeness preserving. Furthermore, the
preprocess often takes more running time than nowadays stateoftheart
planners, like FF, need for solving the entire planning task.
We introduce the notion of relaxed irrelevance, concerning actions which are
never needed within the relaxation that heuristic planners like FF and HSP
use for computing their heuristic values. The idea is to speed up the heuris
tic functions by reducing the action sets considered within the relaxation.
Starting from a sufficient condition for relaxed irrelevance, we introduce
two preprocessing methods for filtering action sets. The first preprocessing
method is proven to be completenesspreserving, and is empirically shown to
terminate fast on most of our testing examples. The second method is fast on
all our testing examples, and is empirically safe. Both methods have drastic
pruning impacts in some domains, speeding up FF's heuristic function, and
in effect the planning process.
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Julie Porteous, Laura Sebastia and Jörg Hoffmann.
On the Extraction, Ordering, and Usage of Landmarks in Planning.
In
A. Cesta and D. Borrajo (eds.),
Proceedings of the 6th European Conference on Planning
(ECP 2001).
2001.
(PS.GZ)
(PDF)
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Jörg Hoffmann.
FF: The Fast-Forward Planning System.
AI Magazine 22 (3), pp. 57-62. 2001.
(PS.GZ)
(PDF)
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Jörg Hoffmann and Bernhard Nebel.
The FF Planning System: Fast Plan Generation Through
Heuristic Search.
Journal of Artificial Intelligence Research 14, pp. 253-302. 2001.
(Show abstract)
(Hide abstract)
(PS.GZ)
(PDF)
We describe and evaluate the algorithmic techniques that are used in
the FF planning system. Like the HSP system, FF relies on forward
state space search, using a heuristic that estimates goal
distances by ignoring delete lists. Unlike HSP's heuristic, our
method does not assume facts to be independent. We introduce a
novel search strategy that combines hill-climbing with systematic
search, and we show how other powerful heuristic information can
be extracted and used to prune the search space. FF was the most
successful automatic planner at the recent AIPS-2000 planning
competition. We review the results of the competition, give data
for other benchmark domains, and investigate the reasons for the
runtime performance of FF compared to HSP.
-
Jörg Hoffmann.
Local Search Topology in Planning Benchmarks: An Empirical
Analysis.
In
Proceedings of the 17th International Joint Conference on
Artificial Intelligence.
Seattle, Washington, USA 2001.
(PS.GZ)
(PDF)
-
Jörg Hoffmann and Bernhard Nebel.
What makes the difference between HSP and FF?
In
IJCAI Workshop on Empirical AI.
Seattle 2001.
(PS.GZ)
(PDF)
-
Jörg Hoffmann and Bernhard Nebel.
Towards Thorough Empirical Methods for AI Planning.
In
IJCAI Workshop on Empirical AI.
Seattle 2001.
(PS.GZ)
(PDF)
-
Jussi Rintanen and Jörg Hoffmann.
An overview of recent algorithms for AI planning.
Künstliche Intelligenz Heft 2/01, pp. 5-11. 2001.
(PS.GZ)
(PDF)
-
Jörg Hoffmann.
A Heuristic for Domain Independent Planning and its Use in an
Enforced Hill-climbing Algorithm.
In
Proceedings of the 14th Workshop on New Results in
Planning, Scheduling and Design
(PuK 2000)
at ECAI 2000, pp. 62-67.
Berlin, Germany 2000.
(PS.GZ)
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Jana Koehler and Jörg Hoffmann.
On the Instantation of ADL Operators Involving Arbitrary
First-Order Formulas.
In
Proceedings of the 14th Workshop on New Results in
Planning, Scheduling and Design
(PuK 2000)
at ECAI 2000, pp. 74-82.
Berlin, Germany 2000.
(PS.GZ)
-
Jörg Hoffmann.
A Heuristic for Domain Independent Planning and its Use in an
Enforced Hill-climbing Algorithm.
In
Proceedings of the 12th International Symposium on
Methodologies for Intelligent Systems.
Charlotte, North Carolina, USA 2000.
(PS.GZ)
-
Jana Koehler and Jörg Hoffmann.
On Reasonable and Forced Goal Orderings and their Use in an
Agenda-Driven Planning Algorithm.
Journal of Artificial Intelligence Research 12, pp. 338-386. 2000.
(PS.GZ)
-
Jana Koehler and Jörg Hoffmann.
Planning with Goal Agendas.
In
Proceedings des 13. Workshops Planen und Konfigurieren
(PuK 1999)
auf der 10. Tagung Expertensysteme
(XPS-99).
Würzburg, Germany 1999.
(PS.GZ)
-
Jörg Hoffmann and Jana Koehler.
A new Method to Query and Index Sets.
In
Proceedings of the 16th International Joint Conference on
Artificial Intelligence (IJCAI 1999).
Stockholm, Sweden 1999.
(PS.GZ)
(extended technical report; PS.GZ)
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Jana Koehler, Bernhard Nebel, Jörg Hoffmann and Yannis Dimopoulos.
Extending Planning Graphs to an ADL Subset.
In
Proc. European Conference on Planning 1997
(ECP-97), pp. 273-285.
Springer-Verlag 1997.
(Show abstract)
(Hide abstract)
(PS.GZ)
We describe an extension of GRAPHPLAN to a subset of ADL that allows conditional and
universally quantified effects in operators in such a way that almost all interesting properties of the
original Graphplan algorithm are preserved.
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Danijel Skocaj, Matej Kristan, Alen Vrecko, Marko Mahnic, Miroslav Janicek, Geert-Jan M. Kruijff, Marc Hanheide, Nick Hawes, Thomas Keller, Michael Zillich and Kai Zhou.
A system for interactive learning in dialogue with a tutor.
In
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011).
2011.
(Show abstract)
(Hide abstract)
(PDF)
In this paper we present representations and mechanisms that facilitate continuous learning of visual concepts in dialogue with a tutor and show the implemented robot system. We present how beliefs about the world are created by processing visual and linguistic information and show how they are used for planning system behaviour with the aim at satisfying its internal drive -- to extend its knowledge. The system facilitates different kinds of learning initiated by the human tutor or by the system itself. We demonstrate these principles in the case of learning about object colours and basic shapes.
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Thomas Keller and Patrick Eyerich.
A Polynomial All Outcome Determinization for Probabilistic
Planning.
In
Fahiem Bacchus, Carmel Domshlak, Stefan Edelkamp and Malte Helmert (eds.),
Proceedings of the 21th International Conference on Automated
Planning and Scheduling (ICAPS 2011), pp. 331-334.
AAAI Press 2011.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Most predominant approaches in probabilistic planning utilize
techniques from the more thoroughly investigated field of
classical planning by determinizing the problem at hand. In
this paper, we present a method to map probabilistic operators
to an equivalent set of probabilistic operators in a novel
normal form, requiring polynomial time and space. From this,
we directly derive a determinization which can be used for,
\eg, replanning strategies incorporating a classical planning
system. Unlike previously described all outcome
determinizations, the number of deterministic operators is not
exponentially but polynomially bounded in the number of
parallel probabilistic effects, enabling the use of more
sophisticated determinization-based techniques in the future.
-
Thomas Keller, Patrick Eyerich and Bernhard Nebel.
Task Planning for an Autonomous Service Robot.
In
Rüdiger Dillmann, Jürgen Beyerer, Uwe Hanebeck and Tanja Schultz (eds.),
Proceedings on the 33rd Annual German Conference on Artificial Intelligence (KI 2010), pp. 358-365.
Springer-Verlag 2010.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
In the DESIRE project an autonomous robot capable of performing service tasks in a typical kitchen environment has been developed. The overall system consists of various loosely coupled subcomponents providing particular features like manipulating objects or recognizing and interacting with humans. To bring all these subcomponents together to act as monolithic system, a high-performance planning system has been implemented. In this paper, we present this system’s basic architecture and some advanced extensions necessary to cope with the various challenges arising in dynamic and uncertain environments like those a real world service robot is usually faced with.
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Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem.
In
Maria Fox and David Poole (eds.),
Proceedings of the Twenty-Fourth AAAI Conference on Artificial
Intelligence (AAAI
2010), pp. 51-58.
AAAI Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
We consider the stochastic variant of the Canadian
Traveler's Problem, a path planning problem where adverse
weather can cause some roads to be untraversable. The agent
does not initially know which roads can be used. However, it
knows a probability distribution for the weather, and it can
observe the status of roads incident to its location. The
objective is to find a policy with low expected travel cost.
We introduce and compare several algorithms for the
stochastic CTP. Unlike the optimistic approach most
commonly considered in the literature, the new approaches we
propose take uncertainty into account explicitly. We show
that this property enables them to generate policies of much
higher quality than the optimistic one, both theoretically
and experimentally.
-
Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem
(Extended Abstract).
In
Ariel Felner and Nathan Sturtevant (eds.),
Proceedings of the Third Annual Symposium on Combinatorial
Search (SoCS 2010), pp. 147-148.
AAAI Press 2010.
Extended abstract of the AAAI paper by the same name.
(PDF)
-
Moritz Göbelbecker, Thomas Keller, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
Coming Up with Good Excuses: What To Do When No Plan Can be Found.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 81-88.
AAAI Press 2010.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
When using a planner-based agent architecture, many things can
go wrong. First and foremost, an agent might fail to execute
one of the planned actions for some reasons. Even more
annoying, however, is a situation where the agent is
incompetent, i.e., unable to come up with a plan. This
might be due to the fact that there are principal reasons that
prohibit a successful plan or simply because the task's
description is incomplete or incorrect. In either case, an
explanation for such a failure would be very helpful. We will
address this problem and provide a formalization of coming
up with excuses for not being able to find a plan. Based
on that, we will present an algorithm that is able to find
excuses and demonstrate that such excuses can be found in
practical settings in reasonable time.
-
Patrick Eyerich, Thomas Keller and Malte Helmert.
High-Quality Policies for the Canadian Traveler's Problem.
In
Proceedings of the
ICAPS-2010
Workshop on Planning and Scheduling Under Uncertainty.
2010.
Superseded by the AAAI 2010 paper by the same name.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
We consider the stochastic variant of the Canadian
Traveler's Problem, a path planning problem where adverse
weather can cause some roads to be untraversable. The agent
does not initially know which roads can be used. However, it
knows a probability distribution for the weather, and it can
observe the status of roads incident to its location. The
objective is to find a policy with low expected travel cost.
We introduce and compare several algorithms for the
stochastic CTP. Unlike the optimistic approach most
commonly considered in the literature, the new approaches we
propose take uncertainty into account explicitly. We show
that this property enables them to generate policies of much
higher quality than the optimistic one, both theoretically
and experimentally.
-
Patrick Eyerich, Thomas Keller and Bernhard Nebel.
Combining Action and Motion Planning via Semantic Attachments.
In
Proceedings of the Workshop on Combining Action and Motion Planning at ICAPS 2010
(CAMP 2010), p. 19.
2010.
Extended Abstract.
(PDF)
(BIB)
-
Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner and Bernhard Nebel.
Semantic Attachments for Domain-Independent Planning Systems.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 114-121.
AAAI Press 2009.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Solving real-world problems using symbolic planning often
requires a simplified formulation of the original problem,
since certain subproblems cannot be represented at all or only
in a way leading to inefficiency. For example, manipulation
planning may appear as a subproblem in a robotic planning
context or a packing problem can be part of a logistics
task. In this paper we propose an extension of PDDL for
specifying semantic attachments. This allows the evaluation of
grounded predicates as well as the change of fluents by
externally specified functions. Furthermore, we describe a
general schema of integrating semantic attachments into a
forward-chaining planner and report on our experience of
adding this extension to the planners FF and Temporal Fast
Downward. Finally, we present some preliminary experiments
using semantic attachments.
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Thomas Keller and Sebastian Kupferschmid.
Automatic Bidding for the Game of Skat.
In
Andreas R. Dengel, Karsten Berns, Thomas M. Breuel, Frank
Bomarius and Thomas R. Roth-Berghofer (eds.),
Proceedings of the 31st Annual German Conference on Artificial Intelligence (KI 2008), pp. 95-102.
Springer-Verlag 2008.
(Show abstract)
(Hide abstract)
(BIB)
(PDF)
In recent years, researchers started to study the game of Skat.
The strength of existing Skat playing programs is definitely the
card play phase. The bidding phase, however, was treated quite
poorly so far. This is a severe drawback since bidding abilities
influence the overall playing performance drastically. In this
paper we present a powerful bidding engine which is based on a
k-nearest neighbor algorithm.
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Christian Dornhege and Alexander Kleiner.
A Frontier-Void-Based Approach for Autonomous Exploration in 3D.
In
Proceedings of the IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR).
2011.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform equipped with a manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.
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Alexander Kleiner, Dali Sun and D. Meyer-Delius.
ARMO: Adaptive Road Map Optimization for Large Robot Teams.
In
Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
2011.
To appear.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Autonomous robot teams that simultaneously dispatch transportation tasks are playing more and more an important role in present logistic centers and manufacturing plants. In this paper we consider the problem of robot motion planning for large robot teams in the industrial domain. We present adaptive road map optimization (ARMO) that is capable of adapting the road map in real time whenever the environment has changed. Based on linear programming, ARMO computes an optimal road map according to current environmental constraints (including human whereabouts) and the current demand for transportation tasks from loading stations in the plant. For detecting dynamic changes, the environment is describe by a grid map augmented with a hidden Markov model (HMM). We show experimentally that ARMO outperforms decoupled planning in terms of computation time and time needed for task completion.
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Alexander Kleiner, A. Kolling, K. Sycara and M. Lewis.
Hierarchical Visibility for Guaranteed Search in Large-Scale Outdoor Terrain.
Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS). 2011.
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Searching for moving targets in large environments is a challenging task that is relevant in several problem domains, such as capturing an invader in a camp, guarding security facilities, and searching for victims in large-scale search and rescue scenarios. The guaranteed search problem is to coordinate the search of a team of agents to guarantee the discovery of all targets. In this paper we present a self-contained solution to this problem in 2.5D real-world domains represented by digital elevation models (DEMs). We introduce hierarchical sampling on DEMs for selecting heuristically the close to minimal set of locations from which the entire surface of the DEM can be guarded. Locations are utilized to form a search graph on which search strategies for mobile agents are computed. For these strategies schedules are derived which include agent paths that are directly executable in the terrain. Presented experimental results demonstrate the performance of the method. The practical feasibility of our approach has been validated during a field experiment at the Gascola robot training site where teams of humans equipped with iPads successfully searched for adversarial and omniscient evaders. The field demonstration is the largest-scale implementation of a guaranteed search algorithm to date.
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Q. Hamp, L. Reindl and Alexander Kleiner.
Lessons Learned from German Research for USAR.
In
Proc. of the IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR).
2011.
To appear.
(Show abstract)
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(PDF)
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We present lessons learned in USAR research within the framework of the German research project I-LOV.
After three years of development first field tests have been carried out by professionals such as the
Rapid Deployment Unit for Salvage Operations Abroad (SEEBA). We present results from evaluating search
teams in simulated USAR scenarios equipped with newly developed technical search means and digital data input terminals developed in the I- LOV project.
In particular, the bioradar, a ground-penetrating radar system for the detection of humanoid movements, a semi-active video probe for rubble pile exploration of more than 10 m length, and the decision support system FRIEDAA were evaluated and compared with conventional search methods. Results of this evaluation indicate that the developed technologies foster advantages in USAR, which are discussed in this paper.
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Alexander Kleiner, Bernhard Nebel and V.A. Ziparo.
A Mechanism for Dynamic Ride Sharing based on Parallel Auctions.
In
Proc. of the 22th International Joint Conference on Artificial Intelligence (IJCAI).
2011.
(to appear).
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Car pollution is one of the major causes of green- house emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Ex- isting efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that minimize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important feature when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.
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D. Meyer-Delius, M. Beinhofer, Alexander Kleiner and W. Burgard.
Reducing the Ambiguity in the Environment by Placing Artificial Landmarks to Improve Mobile Robot Localization.
In
Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA).
2011.
(to appear).
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
Robust and reliable localization is a fundamental prerequisite for successful robot navigation. Although there exist many solutions to the localization problem, environments can be inherently ambiguous so that different robot locations cannot to be distinguished using the sensors of the robot. This is particularly critical in comercial environments, for example in warehouses, containing long corridors and symmetric structures. This ambiguity makes localization approaches more likely to diverge or even prevent the pose of the robot from being uniquely estimated at all. In this paper we propose the utilization of artificial landmarks to reduce the inherent ambiguity in the environment, and present an efficient, localization-oriented approach to landmark placement. Our approach provides us with both the location and number of landmarks to be placed in order to improve the localization performance of the robot in a given environment. Experimental results show that by intelligently placing the landmarks we can improve the localization performance of the robot.
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A. Kolling, Alexander Kleiner, M. Lewis and K. Sycara.
Computing and Executing Strategies for Moving Target Search.
In
Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA).
2011.
(to appear).
(Show abstract)
(Hide abstract)
(PDF)
(BIB)
We address the problem of searching for moving targets in large outdoor environments represented by height maps. To solve the problem we present a complete system that computes from an annotated height map a graph representation and search strategies based on worst-case assumptions about all targets. These strategies are then used to compute a schedule and task assignment for all agents. We improve the graph construction from previous work and for the first time present a method that computes a schedule to minimize the execution time. For this we consider travel times of agents determined by a path planner on the height map. We demonstrate the entire system in a real environment with an area of 700,000m2 in which eight human agents search for two intruders using mobile computing devices (iPads). To the best of our knowledge this is the first demonstration of a search system applied to such a large environment.
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R. Kümmerle, B. Steder, Christian Dornhege, Alexander Kleiner, G. Grisetti and W. Burgard.
Large Scale Graph-based SLAM using Aerial Images as Prior Information.
Autonomous Robots 30, pp. 25-39. 2011.
(Show abstract)
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The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.
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Wei Mou and Alexander Kleiner.
Online Learning Terrain Classification for Adaptive Velocity Control.
In
Proceedings of the IEEE Int. Workshop on Safety, Security and Rescue Robotics
(SSRR 2010).
2010.
(Show abstract)
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(PDF)
(BIB)
Safe teleoperation during critical missions, such as urban search and rescue and bomb disposal, requires careful velocity control when different types of terrain are found in the scenario. This can particularly be challenging when mission time is limited and the operator’s field of view affected.
This paper presents a method for online adapting robot velocities according to the terrain classification from vision and laser readings. The classifier adapts itself to illumination variations, and can be improved online given feedback from the operator.
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Andreas Kolling, Alexander Kleiner, Michael Lewis and Katia Sycara.
Solving Pursuit-Evasion Problems on Height Maps.
In
IEEE International Conference on Robotics and Automation (ICRA 2010)
Workshop: Search and Pursuit/Evasion in the Physical World: Efficiency, Scalability, and Guarantees
(WSPE ICRA 2010).
2010.
(Show abstract)
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(PDF)
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In this paper we present an approach for a pursuit-evasion problem that considers a 2.5d environment
represented by a height map. Such a representation is particularly suitable for large-scale outdoor
pursuit-evasion. By allowing height information we not only capture some aspects of 3d visibility but
can also consider target heights. In our approach we construct a graph representation of the environment
by sampling points and their detection sets which extend the usual notion of visibility. Once a graph
is constructed we compute strategies on this graph using a modification of previous work on graph-searching.
This strategy is converted into robot paths that are planned on the height map by classifying the terrain
appropriately. In experiments we investigate the performance of our approach and provide examples
including a map of a small village with surrounding hills and a sample map with multiple loops and
elevation plateaus. Experiments are carried out with varying sensing ranges as well as target and sensor
heights. To the best of our knowledge the presented approach is the first viable solution to 2.5d
pursuit-evasion with height maps.
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Daniel Maier and Alexander Kleiner.
Improved GPS Sensor Model for Mobile Robots in Urban Terrain.
In
IEEE International Conference on Robotics and Automation
(ICRA 2010), pp. 4385-4390.
2010.
(Video).
(Show abstract)
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(PDF)
(BIB)
Autonomous robot navigation in outdoor scenarios gains increasing importance
in various growing application areas. Whereas in non-urban domains such as deserts
the problem of successful GPS-based navigation appears to be almost solved,
navigation in urban domains particularly in the close vicinity of buildings is
still a challenging problem. In such situations GPS accuracy significantly drops
down due to multiple signal reflections with larger objects causing the so-called multipath error.
In this paper we contribute a novel approach for incorporating multi- path errors into the conventional
GPS sensor model by analyzing environmental structures from online generated point clouds. The approach
has been validated by experimental results conducted with an all- terrain robot operating in scenarios
requiring close- to-building navigation.
Presented results show that positioning accuracy can significantly be improved within urban domains.
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Alexander Kleiner and Christian Dornhege.
Mapping for the Support of First Responders in Critical Domains.
Journal of Intelligent and Robotic Systems (JINT), pp. 1-29. 2010.
(Show abstract)
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In critical domains such as urban search and rescue (USAR), and bomb disposal, the deployment of teleoperated robots is essential to reduce the risk of first responder personnel. Teleoperation is a difficult task, particularly when controlling robots from an isolated safety zone. In general, the operator has to solve simultaneously the problems of mission planning, target identification, robot navigation, and robot control. We introduce a system to support teleoperated navigation with real-time mapping consisting of a two-step scan matching method that re-considers data associations during the search. The algorithm processes data from laser range finder and gyroscope only, thereby it is independent from the robot platform. Furthermore, we introduce a user-guided procedure for improving the global consistency of maps generated by the scan matcher. Globally consistent maps are computed by a graph-based maximum likelihood method that is biased by localizing crucial parts of the scan matcher trajectory on a prior given geo-tiff image. The approach has been implemented as an embedded system and extensively tested on robot platforms designed for teleoperation in critical situations, such as bomb disposal. Furthermore, the system was evaluated in a test maze by first responders during the Disaster City event in Texas 2008.
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Sören Schwertfeger, Adam Jacoff, Chris Scrapper, Johannes Pellenz and Alexander Kleiner.
Evaluation of Maps using Fixed Shapes: The Fiducial Map Metric.
In
Proc. of the Int. Workshop on Performance Metrics for Intelligent Systems (PerMIS), pp. 344-351.
NIST 2010.
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Mapping is an important task for mobile robots. Assessing the quality of those maps is an open topic. A new approach on map evaluation is presented here. It makes use of artificial objects placed in the environment named "Fiducials". Using the known ground-truth positions and the positions of the fiducials identified in the map, a number of quality attributes can be assigned to that map. Depending on the application domain those attributes are weighed to compute a final score. During the 2010 NIST Response Robot Evaluation Exercise at Disaster City an area was populated with fiducials and different mapping runs were performed. The maps generated there are assessed in this paper demonstrating the Fiducial approach. Finally this map scoring algorithm is compared to other approaches found in literature.
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A. Kolling, Alexander Kleiner, M. Lewis and and K. Sycara.
Pursuit-Evasion in 2.5d based on Team-Visibility.
In
Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems
(IROS 2010), pp. 4610-4616.
2010.
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In this paper we present an approach for a pursuit-evasion problem that considers a 2.5d environment represented by a height map. Such a representation is particularly suitable for large-scale outdoor pursuit-evasion, captures some aspects of 3d visibility and can include target heights. In our approach we construct a graph representation of the environment by sampling points and computing detection sets, an extended notion of visibility. Moreover, the constructed graph captures overlaps of detection sets allowing for a coordinated team-based clearing of the environment with robots that move to the sampled points. Once a graph is constructed we compute strategies on it utilizing previous work on graph-searching. This is converted into robot paths that are planned on the height map by classifying the terrain appropriately. In experiments we investigate the performance of our approach and provide examples including a sample map with multiple loops and elevation plateaus and two realistic maps, one of a village and one of a mountain range. To the best of our knowledge the presented approach is the first viable solution to 2.5d pursuit-evasion with height maps.
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Dali Sun, Alexander Kleiner and and C. Schindelhauer.
Decentralized Hash Tables For Mobile Robot Teams Solving Intra-Logistics Tasks.
In
Proceedings of the 9th Int. Joint Conf. on Autonomous Agents and Multiagent Systems
(AAMAS 2010), pp. 923-930.
2010.
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Although a remarkably high degree of automation has been reached in production and intra-logistics nowadays, human labor is still used for transportation using handcarts and forklifts. High labor cost and risk of injury are the undesirable consequences. Alternative approaches in automated warehouses are fixed installed conveyors installed either overhead or floor-based. The drawback of such solutions is the lack of flexibility, which is necessary when the production lines of the company change. Then, such an installation has to be re-built. In this paper, we propose a novel approach of decentralized teams of autonomous robots performing intra-logistics tasks using distributed algorithms. Centralized solutions suffer from limited scalability and have a single point of failure. The task is to transport material between stations keeping the communication network structure intact and most importantly, to facilitate a fair distribution of robots among loading stations. Our approach is motivated by strategies from peer-to-peer-networks and mobile ad-hoc networks. In particular we use an adapted version of distributed heterogeneous hash tables (DHHT) for distributing the tasks and localized communication. Experimental results presented in this paper show that our method reaches a fair distribution of robots over loading stations.
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Alexander Kleiner, Chris Scrapper and Adam Jacoff.
RoboCupRescue Interleague Challenge 2009: Bridging the gap between Simulation and Reality.
In
Proceedings of the Int. Workshop on Performance Metrics for Intelligent Systems
(Permis 2009), pp. 123-129.
NIST 2009.
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Teleoperation is a difficult task, particularly when
controlling robots from an isolated operator station.
In general, the operator has to solve nearly blindly the problems of mission
planning, target identification, robot navigation, and robot control at the same time.
The goal of the proposed system is to support teleoperated navigation
with real-time mapping.
We present a novel scan matching technique that re-considers data
associations during the search, enabling robust pose estimation even under
varying roll and pitch angle of the robot enabling mapping
on rough terrain.
The approach has been implemented as an embedded system and extensively tested
on robot platforms designed for teleoperation in critical situations, such as bomb
disposal.
Furthermore,
the system has been evaluated in a test maze by first responders during
the Disaster City event in Texas 2008.
Finally, experiments conducted within different environments show that
the system yields comparably accurate maps in real-time when
compared to higher sophisticated offline methods, such as Rao-Blackwellized SLAM.
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Alexander Kleiner and Christian Dornhege.
Operator-Assistive Mapping in Harsh Environments.
In
IEEE International Workshop on Safety, Security and Rescue Robotics
(SSRR 2009), pp. 1-6.
2009.
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Teleoperation is a difficult task, particularly when
controlling robots from an isolated operator station.
In general, the operator has to solve nearly blindly the problems of mission
planning, target identification, robot navigation, and robot control at the same time.
The goal of the proposed system is to support teleoperated navigation
with real-time mapping.
We present a novel scan matching technique that re-considers data
associations during the search, enabling robust pose estimation even under
varying roll and pitch angle of the robot enabling mapping
on rough terrain.
The approach has been implemented as an embedded system and extensively tested
on robot platforms designed for teleoperation in critical situations, such as bomb
disposal.
Furthermore,
the system has been evaluated in a test maze by first responders during
the Disaster City event in Texas 2008.
Finally, experiments conducted within different environments show that
the system yields comparably accurate maps in real-time when
compared to higher sophisticated offline methods, such as Rao-Blackwellized SLAM.
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Rainer Kümmerle, Bastian Steder, Christian Dornhege, Michael Ruhnke, Giorgio Grisetti, Cyrill Stachniss and Alexander Kleiner.
On Measuring the Accuracy of SLAM Algorithms.
Autonomous Robots 27 (4), pp. 387-407. 2009.
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In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.
We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.
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Wolfram Burgard, Cyrill Stachniss, Giorgio Grisetti, Bastian Steder, Rainer Kümmerle, Christian Dornhege, Michael Ruhnke, Alexander Kleiner and Juan D. Tardos.
A Comparison of SLAM Algorithms Based on a Graph of Relations.
In
Proceedings of the 2009 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2009), pp. 2089-2095.
IEEE 2009.
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In this paper, we address the problem of creating
an objective benchmark for comparing SLAM approaches.
We propose a framework for analyzing the results of SLAM
approaches based on a metric for measuring the error of the
corrected trajectory. The metric uses only relative relations
between poses and does not rely on a global reference frame.
The idea is related to graph-based SLAM approaches in
the sense that it considers the energy needed to deform the
trajectory estimated by a SLAM approach to the ground
truth trajectory. Our method enables us to compare SLAM
approaches that use different estimation techniques or different
sensor modalities since all computations are made based on the
corrected trajectory of the robot. We provide sets of relative
relations needed to compute our metric for an extensive set
of datasets frequently used in the SLAM community. The
relations have been obtained by manually matching laser-range
observations. We believe that our benchmarking framework
allows the user an easy analysis and objective comparisons
between different SLAM approaches.
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Rainer Kümmerle, Bastian Steder, Christian Dornhege, Alexander Kleiner, Giorgio Grisetti and Wolfram Burgard.
Large Scale Graph-based SLAM using Aerial Images as Prior Information.
In
Proceedings of 2009 Robotics: Science and Systems (RSS 2009).
2009.
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To effectively navigate in their environments and accurately
reach their target locations, mobile robots require a globally
consistent map of the environment. The problem of learning a map
with a mobile robot has been intensively studied in the past and
is usually referred to as the simultaneous localization and
mapping (SLAM) problem. However, existing solutions to the SLAM
problem typically rely on loop-closures to obtain global
consistency and do not exploit prior information even if it is
available. In this paper, we present a novel SLAM approach that
achieves global consistency by utilizing publicly accessible
aerial photographs as prior information. Our approach inserts
correspondences found between three-dimensional laser range
scans and the aerial image as constraints into a graph-based
formulation of the SLAM problem. We evaluate our algorithm based
on large real-world datasets acquired in a mixed in- and outdoor
environment by comparing the global accuracy with
state-of-the-art SLAM approaches and GPS. The experimental
results demonstrate that the maps acquired with our method show
increased global consistency.
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Dali Sun, Alexander Kleiner and T. M. Wendt.
Multi-Robot Range-Only SLAM by Active Sensor Nodes for Urban Search and Rescue.
In
Robocup 2008: Robot Soccer World Cup XII, pp. 318-330.
Springer 2009.
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To jointly map an unknown environment with a team of autonomous robots is a challenging problem, particularly in large environments, as for example the devastated area after a disaster. Under such conditions standard methods for Simultaneous Localization And Mapping (SLAM) are difficult to apply due to possible misinterpretations of sensor data, leading to erroneous data association for loop closure. We consider the problem of multi-robot range-only SLAM for robot teams by solving the data association problem with wireless sensor nodes that we designed for this purpose. The memory of these nodes is utilized for the exchange of map data between multiple robots, facilitating loop-closures on jointly generated maps. We introduce RSLAM, which is a variant of FastSlam, extended for range-only measurements and the multi-robot case. Maps are generated from robot odometry and range estimates, which are computed from the RSSI (Received Signal Strength Indication). The proposed method has been extensively tested in USARSim, which serves as basis for the Virtual Robots competition at RoboCup, and by real-world experiments with a team of mobile robots. The presented results indicates that the approach is capable of building consistent maps in presence of real sensor noise, as well as to improve mapping results of multiple robots by data sharing.
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Alexander Kleiner, G. Steinbauer and F. Wotawa.
Towards automated online diagnosis of robot navigation software.
In
Proc. of Int. Conf. on Simulation, Modeling and Programming for Autonomous Robots (SIMPAR), pp. 159-170.
Springer 2008.
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Control software of autonomous mobile robots comprises a number of software modules that typically interact in a very complex way. Their proper interaction and the robustness of each single module strongly influences the safety during navigation in the field. Particularly in unstructured environments, unforeseen situations are likely to occur, causing erroneous behaviors of the robot. The proper handling of such situations requires an understanding of cause and effect within the complex interactions of the system. In this paper we present an approach which is able to automatically derive a model of the communication behavior within a component-orientated control software. The model can be used for online diagnosis in order to increase system robustness during runtime. We demonstrate model learning and system diagnosis on three different robot systems which were controlled by software modules communicating based on the widely used IPC (Inter Process Communication) standard. The demonstrated learning and diagnosis was carried out without any a priori knowledge about the systems.
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Christian Dornhege and Alexander Kleiner.
Fully autonomous planning and obstacle negotiation on rough terrain using behavior maps.
In
Video Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2007).
San Diego, California 2007.
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Christian Dornhege and Alexander Kleiner.
Behavior maps for online planning of obstacle negotiation and climbing on rough terrain.
In
Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2007), pp. 3005-3011.
2007.
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To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.
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Alexander Kleiner and R. Kümmerle.
Genetic MRF model optimization for real-time victim detection in Search and Rescue.
In
Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2007), pp. 3025-3030.
2007.
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One primary goal in rescue robotics is to deploy a team of robots for coordinated victim search after a disaster. This requires robots to perform subtasks, such as victim detection, in real-time. Human detection by computationally cheap techniques, such as color thresholding, turn out to produce a large number of false-positives. Markov Random Fields (MRFs) can be utilized to combine the local evidence of multiple weak classifiers in order to improve the detection rate. However, inference in MRFs is computational expensive. In this paper we present a novel approach for the genetic optimizing of the building process of MRF models. The genetic algorithm determines offline relevant neighborhood relations with respect to the data, which are then utilized for generating efficient MRF models from video streams during runtime. Experimental results clearly show that compared to a Support Vector Machine (SVM) based classifier, the optimized MRF models significantly reduce the false-positive rate. Furthermore, the optimized models turned out to be up to five times faster then the non-optimized ones at nearly the same detection rate.
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Alexander Kleiner and Christian Dornhege.
Real-time Localization and Elevation Mapping within Urban Search and Rescue Scenarios.
Journal of Field Robotics 24, pp. 723-745. 2007.
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Urban Search And Rescue (USAR) is a time critical task. Rescue teams have to explore a large terrain within a short amount of time in order to locate survivors after a disaster. One goal in Rescue Robotics is to have a team of heterogeneous robots that explore autonomously, or partially guided by an incident commander, the disaster area. Their task is to jointly create a map of the terrain and to register victim locations, which can further be utilized by human task forces for rescue. Basically, the robots have to solve autonomously in real-time the problem of Simultaneous Localization and Mapping (SLAM), consisting of a continuous state estimation problem and a discrete data association problem. Extraordinary circumstances after a real disaster make it very hard to apply common techniques. Many of these have been developed under strong assumptions, for example, they require polygonal structures, such as typically found in office-like environments. Furthermore, most techniques are not deployable in real-time. In this paper we propose real-time solutions for localization and mapping, which all have been extensively evaluated within the test arenas of the National Institute of Standards and Technology (NIST). We specifically deal with the problems of vision-based pose tracking on tracked vehicles, the building of globally consistent maps based on a network of RFID tags, and the building of elevation maps from readings of a tilted Laser Range Finder (LRF). Our results show that these methods lead under modest computational requirements to good results within the utilized testing arenas.
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S. Balakirsky, S. Carpin, Alexander Kleiner, M. Lewis, A. Visser, J. Wang and V.A. Ziparo.
Towards heterogeneous robot teams for disaster mitigation: Results and Performance Metrics from Robocup Rescue.
Journal of Field Robotics 24, pp. 943-967. 2007.
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Urban Search And Rescue is a growing area of robotic research. The RoboCup Federation has recognized this, and has created the new Virtual Robots competition to complement its existing physical robot and agent competitions. In order to successfully compete in this competition, teams need to field multi-robot solutions that cooperatively explore and map an environment while searching for victims. This paper presents the results of the first annual RoboCup Rescue Virtual competition. It provides details on the metrics used to judge the contestants as well as summaries of the algorithms used by the top four teams. This allows readers to compare and contrast these effective approaches. Furthermore, the simulation engine itself is examined and real-world validation results on the engine and algorithms are offered.
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V.A. Ziparo, Alexander Kleiner, L. Marchetti, A. Farinelli and and D. Nardi.
Cooperative Exploration for USAR Robots with Indirect Communication.
In
Proceedings of the 6th IFAC Symposium on Intelligent Autonomous
Vehicles (IAV 2007).
2007.
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To coordinate a team of robots for exploration is a challenging problem, particularly in unstructured areas, as for example post-disaster scenarios where direct communication is severely constrained. Furthermore, conventional methods of SLAM, e.g. those performing data association based on visual features, are doomed to fail due to bad visibility caused by smoke and fire. We use indirect communication (based on RFIDs), to share knowledge and use a gradient-like local search to direct robots towards interesting areas. To share a common frame of reference among robots we use a feature based SLAM approach (where features are RFIDs). The approach has been evaluated on a 3D simulation based on USARSim.
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Vittorio Ziparo, Alexander Kleiner, Bernhard Nebel and Daniele Nardi.
RFID-Based Exploration for Large Robot Teams.
In
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2007), pp. 4606-4613.
Rome, Italy 2007.
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Alexander Kleiner, Christian Dornhege and Dali Sun.
Mapping disaster areas jointly: RFID -Coordinated SLAM by Humans and Robots.
In
Proceedings of the IEEE International Workshop on Safety, Security
and Rescue Robotics (SSRR 2007), pp. 1-6.
2007.
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We consider the problem of jointly performing SLAM by humans and robots in Urban Search And Rescue (USAR) scenarios. In this context, SLAM is a challenging task. First, places are hardly re-observable by vision techniques since visibility might be affected by smoke and fire. Second, loop-closure is cumbersome due to the fact that firemen will intentionally try to avoid performing loops when facing the reality of emergency response, e.g.USAR, while they are searching for victims. Furthermore, there might be places that are only accessible to robots, making it necessary to integrate humans and robots into one team for mapping the area after a disaster. In this paper, we introduce a method for jointly correcting individual trajectories of humans and robots by utilizing RFID technology for data association. Hereby the poses of humans and robots are tracked by a PDR (Pedestrian Dead Reckoning), and slippage sensitive odometry, respectively. We conducted extensive experiments with a team of humans, and a human-robot team within a semi-outdoor environment. Results from these experiments show that the introduced method allows to improve single trajectories based on the joint graph, even if they do not contain any loop.
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H. Kenn and Alexander Kleiner.
Towards the Integration of Real-Time Real-World Data in Urban Search and Rescue Simulation.
In
MobileResponse, pp. 106-115.
Springer 2007.
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The coordinated reaction to a large-scale disaster is a challenging research problem. The Robocup rescue simulation league addresses this research problem but is currently lacking an interface to real-world real-time data to test the validity of both simulation and simulated reactions. In this paper, we describe a wearable-computing-based real world interface to the Robocup Resuce simulation software and provide some updated results of preliminary evaluations.
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Alexander Kleiner and Dali Sun.
Decentralized SLAM for Pedestrians without direct Communication.
In
Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2007), pp. 1461-1466.
2007.
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We consider the problem of Decentralized Simultaneous Localization And Mapping (DSLAM) for pedestrians in the context of Urban Search And Rescue (USAR). In this context, DSLAM is a challenging task. First, data exchange fails due to cut off communication links. Second, loop-closure is cumbersome due to the fact that fireman will intentionally try to avoid performing loops, when facing the reality of emergency response, e.g. while they are searching for victims. In this paper, we introduce a solution to this problem based on the non-selfish sharing of information between pedestrians for loop-closure. We introduce a novel DSLAM method which is based on data exchange and association via RFID technology, not requiring any radio communication. The approach has been evaluated within both outdoor and semi-indoor environments. The presented results show that sharing information between single pedestrians allows to optimize globally their individual paths, even if they are not able to communicate directly.
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Alexander Kleiner, Johann Prediger and Bernhard Nebel.
RFID Technology-based Exploration and SLAM for Search And Rescue.
In
Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2006), pp. 4054-4059.
Beijing, China 2006.
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Robot search and rescue is a time critical task, i.e.
a large terrain has to be explored by multiple robots within
a short amount of time. The efficiency of exploration depends
mainly on the coordination between the robots and hence on the
reliability of communication, which considerably suffers under
the hostile conditions encountered after a disaster. Furthermore,
rescue robots have to generate a map of the environment which
has to be sufficiently accurate for reporting the locations of
victims to human task forces. Basically, the robots have to
solve autonomously in real-time the problem of Simultaneous
Localization and Mapping (SLAM).
This paper proposes a novel method for real-time exploration
and SLAM based on RFID tags that are autonomously distributed
in the environment. We utilized the algorithm of Lu
and Milios [8] for calculating globally consistent maps from
detected RFID tags. Furthermore we show how RFID tags can
be used for coordinating the exploration of multiple robots.
Results from experiments conducted in the simulation and
on a robot show that our approach allows the computationally
efficient construction of a map within harsh environments, and
coordinated exploration of a team of robots.
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Alexander Kleiner, Christian Dornhege, Rainer Kuemmerle, Michael Ruhnke, Bastian Steder, Bernhard Nebel, Patrick Doherty, Mariusz Wzorek, Piotr Rudol, Gianpaolo Conte, S. Durante and D. Lundstrom.
RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
In
CDROM Proceedings of the International RoboCup Symposium '05.
Bremen, Germany 2006.
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This paper describes the approach of the RescueRobots Freiburg team,
which is a team of students from the University of Freiburg that originates from
the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team
(RoboCupRescue Simulation). Furthermore we introduce linkMAV, a micro aerial
vehicle platform.
Our approach covers RFID-based SLAM and exploration, autonomous detection
of relevant 3D structures, visual odometry, and autonomous victim identification.
Furthermore, we introduce a custom made 3D Laser Range Finder (LRF) and a
novel mechanism for the active distribution of RFID tags.
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Alexander Kleiner and Vittorio Ziparo.
RoboCupRescue - Simulation League Team RescueRobots Freiburg (Germany), Team Description Paper.
In
CDROM Proceedings of the International RoboCup Symposium '06.
Bremen, Germany 2006.
(PDF)
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Christian Dornhege and Alexander Kleiner.
Visual Odometry for Tracked Vehicles.
In
Proceedings of the IEEE International Workshop on Safety, Security and Rescue Robotics (SSRR 2006).
2006.
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Localization and mapping on autonomous robots typically requires a good pose estimate, which is hard to acquire if the vehicle is tracked. In this paper we describe a solution to the pose estimation problem by utilizing a consumer-quality camera and an Inertial Measurement Unit (IMU). The basic idea is to continuously track salient features with the KLT feature tracker over multiple images taken by the camera and to extract from the tracked features image vectors resulting from the robot's motion. Each image vector is taken for a voting that best explains the robot's motion. Image vectors vote according to a previously trained ^\m2102tile coding^\m2112 classificator that assigns to each possible image vector a translation probability. Our results show that the proposed single camera solution leads to sufficiently accurate pose estimates of the tracked vehicle.
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Alexander Kleiner, N. Behrens and H. Kenn.
Wearable computing meets multiagent systems: A real-world interface for the RoboCupRescue simulation platform.
In
First International Workshop on Agent Technology for Disaster Management at AAMAS06, pp. 116-123.
AAMAS Press 2006.
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One big challenge in disaster response is to get an overview over the degree of damage and to provide this information, together with optimized plans for rescue missions, back to teams in the field. Collapsing infrastructure, limited visibility due to smoke and dust, and overloaded communication lines make it nearly impossible for rescue teams to report the total situation consistently. This problem can only be solved by efficiently integrating data of ^\m2102many^\m2112 observers into a single consistent view. A Global Positioning System (GPS) device in conjunction with a communication device, and sensors or simple input methods for reporting observations, offer a realistic chance to solve the data integration problem. We propose preliminary results from a wearable computing device, acquiring disaster relevant data, such as locations of victims and blockades, and show the data integration into the RoboCupRescue Simulation platform, which is a benchmark for MAS within the RoboCup competitions. We show exemplarily how the data can consistently be integrated and how rescue missions can be optimized by solutions developed on the RoboCupRescue simulation platform. The preliminary results indicate that nowadays wearable computing technology combined with MAS technology can serve as a powerful tool for Urban Search and Rescue (USAR).
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Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger, Joerg Stueckler and Bernhard Nebel.
Successful Search and Rescue in Simulated Disaster Areas.
In
Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
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RoboCupRescue Simulation is a large-scale multi-agent simulation
of urban disasters where, in order to save lives and minimize damage, rescue
teams must effectively cooperate despite sensing and communication limitations.
This paper presents the comprehensive search and rescue approach of the ResQ
Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup
2004.
Specific contributions include the predictions of travel costs and civilian lifetime,
the efficient coordination of an active disaster space exploration, as well as
an any-time rescue sequence optimization based on a genetic algorithm.
We compare the performances of our team and others in terms of their capability
of extinguishing fires, freeing roads from debris, disaster space exploration, and
civilian rescue. The evaluation is carried out with information extracted from
simulation log files gathered during RoboCup 2004. Our results clearly explain
the success of our team, and also confirm the scientific approaches proposed in
this paper.
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Alexander Kleiner, Bastian Steder, Christian Dornhege, Daniel Hoefler, Daniel Meyer-Delius, Johann Prediger, Joerg Stueckler, Kolja Glogowski, Markus Thurner, Matthias Luber, Michael Schnell, Rainer Kuemmerle, Timothy Burk, Tobias Braeuer and Bernhard Nebel.
RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
In
CDROM Proceedings of the International RoboCup Symposium '05.
Osaka, Japan 2005.
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This paper describes the approach of the RescueRobots Freiburg team.
RescueRobots Freiburg is a team of students from the university of Freiburg, that
originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ
Freiburg team (RoboCupRescue Simulation).
Due to the high versatility of the RoboCupRescue competition we tackle the three
arenas by a a twofold approach: On the one hand we want to introduce robust
vehicles that can safely be teleoperated through rubble and building debris while
constructing three-dimensional maps of the environment. On the other hand we
want to introduce a team of autonomous robots that quickly explore a large terrain
while building a two-dimensional map. This two solutions are particularly wellsuited
for the red and yellow arena, respectively. Our solution for the orange arena
will finally be decided between these two, depending on the capabilities of both
approaches at the venue.
In this paper, we introduce some preliminary results that we achieved so far from
map building, localization, and autonomous victim identification. Furthermore
we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism
for the active distribution of RFID tags.
1 Introduction
RescueRobots Freiburg is a team of students from the university of Freiburg. The team
originates from the former CS Freiburg team[6], which won three times the RoboCup
world championship in the RoboCupSoccer F2000 league, and the ResQ Freiburg team[2],
which won the last RoboCup world championship in the RoboCupRescue Simulation
league. The team approach proposed in this paper is based on experiences gathered at
RoboCup during the last six years.
Due to the high versatility of the RoboCupRescue competition we tackle the three
arenas by a twofold approach: On the one hand we want to introduce a vehicle that
can safely be teleoperated through rubble and building debris while constructing threedimensional
maps of the environment. On the other hand we want to introduce an autonomous
team of robots that quickly explore a large terrain while building a twodimensional
map. This two solutions are particularly well-suited for the red and yellow
arena, respectively. Our solution for the orange arena will finally be decided between
these two, depending on the capabilities of both approaches at the venue.
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Alexander Kleiner.
Game AI: The shrinking gap between computer games and AI systems Ambient Intelligence.
Ambient Intelligence:The evolution of technology, communication and cognition towards the future of human-computer interaction. 2005.
(PDF)
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Timo Nuessle, Alexander Kleiner and Michael Brenner.
Approaching Urban Disaster Reality: The ResQ Firesimulator.
In
Proceedings of the International RoboCup Symposium '04.
Lisbon, Portugal 2004.
(PDF)
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Alexander Kleiner, Michael Brenner, Tobias Braeuer, Christian Dornhege, Moritz Göbelbecker, Matthias Luber, Johann Prediger and Joerg Stueckler.
ResQ Freiburg: Team Description and Evaluation, Team Description Paper, Rescue Simulation League.
In
CDROM Proceedings of the International RoboCup Symposium '04.
Lisbon, Portugal 2004.
(PDF)
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Erik Schulenburg, Thilo Weigel and Alexander Kleiner.
Self-Localization in Dynamic Environments based on Laser and Vision
Data.
In
Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS'03), pp. 998-1004.
Las Vegas, USA 2003.
(PS.GZ)
(PDF)
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Alexander Kleiner and Thorsten Buchheim.
A Plugin-Based Architecture For Simulation In The F2000 League.
In
Proceedings of the International RoboCup Symposium '03.
Padova, Italy 2003.
(PS.GZ)
(PDF)
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Alexander Kleiner, Markus Dietl and Bernhard Nebel.
Towards a Life-Long Learning Soccer Agent.
In
Proceedings of the International RoboCup Symposium '02.
Fukuoka, Japan 2002.
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One problem in robotic soccer (and in robotics in general) is to adapt
skills and the overall behavior to a changing environment and to hardware
improvements. We applied hierarchical reinforcement learning in an SMDP
framework learning on all levels simultaneously. As our experiments show,
learning simultaneously on the skill level and on the skill selection level
is advantageous since it allows for a smooth adaption to a changing
environment. Furthermore, the skills we trained turn also out to be quite
competitive when run on the real robotic players of the players of our
CS Freiburg team.
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Thilo Weigel, Jens-Steffen Gutmann, Markus Dietl, Alexander Kleiner and Bernhard Nebel.
CS Freiburg: Coordinating Robots for Successful Soccer Playing.
IEEE Transactions on Robotics and Automation 18 (5), pp. 685-699. 2002.
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Robotic soccer is a challenging research domain because many
different research areas have to be addressed in order to create a
successful team of robot players. This paper presents the CS
Freiburg team, the winner in the middle size league at RoboCup
1998, 2000 and 2001. The paper focuses on multi-agent coordination
for both perception and action. The contributions of this work are
new methods for tracking ball and players observed by multiple
robots, team coordination methods for strategic team formation and
dynamic role assignment, a rich set of basic skills allowing to
respond to large range of situations in an appropriate way, an
action selection method based on behavior networks as well as a
method to learn the skills and their selection. As demonstrated by
evaluations of the different methods and by the success of the team,
these methods permit the creation of a multi-robot group, which is
able to play soccer successfully. In addition, the developed methods
promise to advance the state of the art in the multi-robot field.
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Thilo Weigel, Alexander Kleiner, Florian Diesch, Markus Dietl, Jens-Steffen Gutmann, Bernhard Nebel, Patrick Stiegeler and Boris Szerbakowski.
CS Freiburg 2001.
In
International RoboCup Symposium 2001.
2001.
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The CS Freiburg team has become F2000 champion the third time in the
history of RoboCup. The success of our team can probably be
attributed to its robust sensor interpretation and its team play. In
this paper, we will focus on new developments in our vision system,
in our path planner, and in the cooperation component.
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Sebastian Kupferschmid and Martin Wehrle.
Abstractions and Pattern Databases: The Quest for Succinctness and Accuracy.
In
Parosh A. Abdulla and K. Rustan M. Leino (ed.),
Proceedings of the 17th International Conference on
Tools and Algorithms for the Construction and Analysis of Systems
(TACAS
2011), pp. 276-290.
Springer-Verlag 2011.
To appear.
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Directed model checking is a well-established technique for
detecting error states in concurrent systems efficiently. As
error traces are important for debugging purposes, it is
preferable to find as short error traces as possible. A wide
spread method to find provably shortest error traces is
to apply the A* search algorithm with distance heuristics
that never overestimate the real error distance. An important
class of such distance estimators is the class of
pattern database heuristics, which are built on
abstractions of the system under consideration. In this paper,
we propose a systematic approach for the construction of
pattern database heuristics. We formally define a concept to
measure the accuracy of abstractions. Based on this technique,
we address the challenge of finding abstractions that are
succinct on the one hand, and accurate to produce informed
pattern databases on the other hand. We evaluate our approach
on large and complex industrial problems. The experiments show
that the resulting distance heuristic impressively advances
the state of the art.
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Martin Wehrle and Sebastian Kupferschmid.
Context-Enhanced Directed Model Checking.
In
Jaco van de Pol and Michael Weber (eds.),
Proceedings of the 17th International SPIN Workshop on Model Checking Software
(SPIN 2010), pp. 88-105.
Springer-Verlag 2010.
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Directed model checking is a well-established technique to
efficiently tackle the state explosion problem when the aim is
to find error states in concurrent systems. Although directed
model checking has proved to be very successful in the past,
additional search techniques provide much potential to
efficiently handle larger and larger systems. In this work, we
propose a novel technique for traversing the state space based
on interference contexts. The basic idea is to preferably
explore transitions that interfere with previously applied
transitions, whereas other transitions are deferred
accordingly. Our approach is orthogonal to the model checking
process and can be applied to a wide range of search methods.
We have implemented our method and empirically evaluated its
potential on a range of non-trivial case studies. Compared to
standard model checking techniques, we are able to detect
subtle bugs with shorter error traces, consuming less memory
and time.
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Martin Wehrle, Sebastian Kupferschmid and Andreas Podelski.
Transition-based Directed Model Checking.
In
Stefan Kowalewski and Anna Philippou (eds.),
Proceedings of the 15th International Conference on Tools and
Algorithms for the Construction and Analysis of Systems (TACAS
2009), pp. 186-200.
Springer-Verlag 2009.
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Directed model checking is a well-established technique that
is tailored to fast detection of system states that violate a
given safety property. This is achieved by influencing the
order in which states are explored during the state space
traversal. The order is typically determined by an abstract
distance function that estimates a state's distance to a
nearest error state. In this paper, we propose a general
enhancement to directed model checking based on the evaluation
of state transitions. We present a schema,
parametrized by an abstract distance function, to evaluate
transitions and propose a new method for the state space
traversal. Our framework can be applied automatically to a
wide range of abstract distance functions. The empirical
evaluation impressively shows its practical potential.
Apparently, the new method identifies a sweet spot in the
trade-off between scalability (memory consumption) and short
error traces.
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Thomas Keller and Sebastian Kupferschmid.
Automatic Bidding for the Game of Skat.
In
Andreas R. Dengel, Karsten Berns, Thomas M. Breuel, Frank
Bomarius and Thomas R. Roth-Berghofer (eds.),
Proceedings of the 31st Annual German Conference on Artificial Intelligence (KI 2008), pp. 95-102.
Springer-Verlag 2008.
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In recent years, researchers started to study the game of Skat.
The strength of existing Skat playing programs is definitely the
card play phase. The bidding phase, however, was treated quite
poorly so far. This is a severe drawback since bidding abilities
influence the overall playing performance drastically. In this
paper we present a powerful bidding engine which is based on a
k-nearest neighbor algorithm.
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Martin Wehrle, Sebastian Kupferschmid and Andreas Podelski.
Useless Actions are Useful.
In
Jussi Rintanen, Bernhard Nebel, J. Christopher Beck and Eric Hansen (eds.),
Proceedings of the 18th International Conference on Automated
Planning and Scheduling (ICAPS 2008), pp. 388-395.
AAAI Press 2008.
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Planning as heuristic search is a powerful approach to solving
domain independent planning problems. In recent years, various
successful heuristics and planners like FF, LPG, Fast Downward
or SGPlan have been proposed in this context. However, as
heuristics only estimate the distance to goal states, a
general problem of heuristic search is the existence of
plateaus in the search space topology which can cause the
search process to degenerate. Additional techniques like
helpful actions or preferred operators that evaluate the
"usefulness" of actions are often successful strategies to
support the search in such situations.
In this paper, we introduce a general method to evaluate the
usefulness of actions. We propose a technique to enhance
heuristic search by identifying "useless" actions that are
not needed to find optimal plans. In contrast to helpful
actions or preferred operators that are specific to the FF
and Causal Graph heuristic, respectively, our method can be
combined with arbitrary heuristics. We show that this
technique often yields significant performance improvements.
-
Sebastian Kupferschmid, Martin Wehrle, Bernhard Nebel and Andreas Podelski.
Faster than Uppaal?
In
A. Gupta and S. Malik (eds.),
Proceedings of the 20th International Conference on Computer Aided
Verification (CAV 2008), pp. 552-555.
Springer-Verlag 2008.
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It is probably very hard to develop a new model checker that
is faster than Uppaal for verifying correct timed automata. In
fact, our tool Mcta does not even try to compete with Uppaal
in this (i.e., Uppaal's) arena. Instead, Mcta is geared
towards analyzing incorrect specifications of timed automata.
It returns (shorter) error traces faster.
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Sebastian Kupferschmid, Jörg Hoffmann and Kim G. Larsen.
Fast Directed Model Checking via Russian Doll Abstraction.
In
C. R. Ramakrishnan and J. Rehof (eds.),
Proceedings of the 14th International Conference on Tools and
Algorithms for the Construction and Analysis of Systems (TACAS 2008), pp. 203-217.
Springer-Verlag 2008.
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Directed model checking aims at speeding up the search for bugs
in a system through the use of heuristic functions. Such a
function maps states to integers, estimating the state's
distance to the nearest error state. The search gives a
preference to states with lower estimates. The key issue is how
to generate good heuristic functions, i.e., functions that guide
the search quickly to an error state. An arsenal of heuristic
functions has been developed in recent years. Significant
progress was made, but many problems still prove to be
notoriously hard. In particular, a body of work describes
heuristic functions for model checking timed automata in Uppaal,
and tested them on a certain set of benchmarks. Into this
arsenal we add another heuristic function. With previous
heuristics, for the largest of the benchmarks it was only just
possible to find some (unnecessarily long) error path. With
the new heuristic, we can find provably shortest error paths for
these benchmarks in a matter of seconds. The heuristic
function is based on a kind of Russian Doll principle, where the
heuristic for a given problem arises through using Uppaal itself
for the complete exploration of a simplified instance of the
same problem. The simplification consists in removing those
parts from the problem that are distant from the error property.
As our empirical results confirm, this simplification often
preserves the characteristic structure leading to the error.
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Henning Dierks, Sebastian Kupferschmid and Kim G. Larsen.
Automatic Abstraction Refinement for Timed Automata.
In
Jean-François Raskin and P. S. Thiagarajan (eds.),
Proceedings of the 5th International Conference on
Formal Modelling and Analysis of Timed Systems
(FORMATS 2007), pp. 114-129.
Springer-Verlag 2007.
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We present a fully automatic approach for counterexample guided
abstraction refinement of real-time systems modelled in a subset
of timed automata. Our approach is implemented in the Moby/RT
tool environment, which is a CASE tool for embedded system
specifications. Verification in Moby/RT is done by constructing
abstractions of the semantics in terms of timed automata which
are fed into the model checker Uppaal. Since the abstractions
are over-approximations, absence of abstract counterexamples
implies a valid result for the full model. Our new approach
deals with the situation in which an abstract counterexample is
found by Uppaal. The generated abstract counterexample is used
to construct either a concrete counterexample for the full model
or to identify a slightly refined abstraction in which the found
spurious counterexample cannot occur anymore. Hence, the
approach allows for a fully automatic abstraction refinement
loop starting from the coarsest abstraction towards an
abstraction for which a valid verification result is found.
Nontrivial case studies demonstrate that this approach computes
small abstractions fast without any user interaction.
-
Sebastian Kupferschmid, Klaus Dräger, Jörg Hoffmann, Bernd Finkbeiner, Henning Dierks, Andreas Podelski and Gerd Behrmann.
UPPAAL/DMC - Abstraction-based Heuristics for Directed Model Checking.
In
Orna Grumberg and Michael Huth (eds.),
Proceedings of the 13th International Conference on Tools
and Algorithms for the Construction and Analysis of Systems
(TACAS 2007), pp. 679-682.
Springer-Verlag 2007.
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Uppaal/DMC is an extension of Uppaal that provides generic
heuristics for directed model checking. In this approach, the
traversal of the state space is guided by a heuristic function
which estimates the distance of a search state to the nearest
error state. Our tool combines two recent approaches to design
such estimation functions. Both are based on computing an
abstraction of the system and using the error distance in this
abstraction as the heuristic value. The abstractions, and thus
the heuristic functions, are generated fully automatically and
do not need any additional user input. Uppaal/DMC needs less
time and memory to find shorter error paths than Uppaal's
standard search methods.
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Jörg Hoffmann, Jan-Georg Smaus, Andrey Rybalchenko, Sebastian Kupferschmid and Andreas Podelski.
Using Predicate Abstraction to Generate Heuristic Functions in
Uppaal.
In
Stefan Edelkamp and Alessio Lomuscio (eds.),
Proceedings of the 4th Workshop on Model Checking and Artificial Intelligence
(MoChArt 2006), pp. 51-66.
Springer-Verlag 2006.
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We focus on checking safety properties in networks of extended
timed automata, with the well-known Uppaal system. We show how
to use predicate abstraction, in the sense used in model
checking, to generate search guidance, in the sense used in
Artificial Intelligence (AI). This contributes another family
of heuristic functions to the growing body of work on
directed model checking. The overall methodology
follows the pattern database approach from AI: the
abstract state space is exhaustively built in a pre-process,
and used as a lookup table during search. While typically
pattern databases use rather primitive abstractions ignoring
some of the relevant symbols, we use predicate
abstraction, dividing the state space into equivalence
classes with respect to a list of logical expressions
(predicates). We empirically explore the behavior of the
resulting family of heuristics, in a meaningful set of
benchmarks. In particular, while several challenges remain
open, we show that one can easily obtain heuristic functions
that are competitive with the state-of-the-art in directed
model checking.
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Sebastian Kupferschmid and Malte Helmert.
A Skat Player Based on Monte Carlo Simulation.
In
H. Jaap van den Herik, Paolo Ciancarini and H. H. L. M. Donkers (eds.),
Proceedings of the Fifth International Conference on
Computer and Games (CG 2006), pp. 135-147.
Springer-Verlag 2006.
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We apply Monte Carlo simulation and alpha-beta search to the
card game of Skat, which is similar to Bridge, but
different enough to require some new algorithmic ideas besides
the techniques developed for Bridge. Our Skat-playing program
integrates well-known techniques such as move ordering
with two new search enhancements. Quasi-symmetry
reduction generalizes symmetry reductions, popularized by
Ginsberg's Partition Search algorithm, to search states which
are "almost equivalent". Adversarial heuristics
generalize ideas from single-agent search algorithms like A* to
two-player games, leading to guaranteed lower and upper bounds
for the score of a game position. Combining these techniques
with state-of-the-art tree search algorithms, our program
determines the game-theoretical value of a typical Skat hand
(with perfect information) in 10 milliseconds.
-
Sebastian Kupferschmid, Jörg Hoffmann, Henning Dierks and Gerd Behrmann.
Adapting an AI Planning Heuristic for Directed Model Checking.
In
Antti Valmari (ed.),
Proceedings of the 13th International SPIN Workshop on Model Checking Software
(SPIN 2006), pp. 35-52.
Springer-Verlag 2006.
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There is a growing body of work on directed model checking,
which improves the falsification of safety properties by
providing heuristic functions that can guide the search
quickly towards short error paths. Techniques
of this kind have also been made very successful in the area of
AI Planning. Our main technical contribution is the adaptation
of the most successful heuristic function from AI Planning to
the model checking context, yielding a new heuristic for
directed model checking. The heuristic is based on solving an
abstracted problem in every search state. We adapt the
abstraction and its solution to networks of communicating
automata annotated with (constraints and effects on) integer
variables. Since our ultimate goal in this research is to also
take into account clock variables, as used in timed automata,
our techniques are implemented inside Uppaal. We run
experiments in some toy benchmarks for timed automata, and in
two timed automata case studies originating from an industrial
project. Compared to both blind search and some previously
proposed heuristic functions, we consistently obtain
significant, sometimes dramatic, search space reductions,
resulting in likewise strong reductions of runtime and memory
requirements.
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Jörg Hoffmann and Sebastian Kupferschmid.
A Covering Problem for Hypercubes.
In
Leslie Pack Kaelbling and Alessandro Saffiotti (eds.),
Poster Proceedings of the 19th International Joint
Conference on Artificial Intelligence (IJCAI 2005), pp. 1523-1524.
2005.
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We introduce a new NP-complete problem asking if a "query"
hypercube is (not) covered by a set of other "evidence"
hypercubes. This comes down to a form of constraint reasoning
asking for the satisfiability of a CNF formula where the
logical atoms are inequalities over single variables, with
possibly infinite variable domains. We empirically investigate
the location of the phase transition regions in two random
distributions of problem instances. We introduce a solution
method that iteratively constructs a representation of the
non-covered part of the query cube. In particular, the method
is not based on backtracking. Our experiments show that the
method is, in a significant range of instances, superior to the
backtracking method that results from translation to SAT, and
application of a state-of-the-art DP-based SAT solver.
-
Rüdiger Ehlers, Robert Mattmüller and Hans-Jörg Peter.
Combining Symbolic Representations for Solving Timed Games.
In
Proceedings of the 8th International Conference on Formal Modelling and Analysis of Timed Systems
(FORMATS 2010).
2010.
To appear.
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We present a general approach to combine symbolic state space
representations for the discrete and continuous parts in the
synthesis of winning strategies for timed reachability
games. The combination is based on abstraction refinement
where discrete symbolic techniques are used to produce a
sequence of abstract timed game automata. After each
refinement step, the resulting abstraction is used for
computing an under- and an over-approximation of the timed
winning states. The key idea is to identify large relevant and
irrelevant parts of the precise weakest winning strategy
already on coarse, and therefore simple, abstractions. If
neither the existence nor nonexistence of a winning strategy
can be established in the approximations, we use them to guide
the refinement process. Based on a prototype that combines
binary decision diagrams and difference bound matrices, we
experimentally evaluate the technique on standard benchmarks
from timed controller synthesis. The results clearly
demonstrate the potential of the new approach concerning
running time and memory consumption compared to the classical
on-the-fly algorithm implemented in UPPAAL-Tiga.
-
J. Benton, Kartik Talamadupula, Patrick Eyerich, Robert Mattmüller and Subbarao Kambhampati.
G-value Plateaus: A Challenge for Planning.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 259-262.
AAAI Press 2010.
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Recent years have seen the development of several scalable
planners, many of which follow the string of successes found
in using heuristic, best-first search methods. While this
provides positive reinforcement for continuing work along
these lines, fundamental problems arise when handling
objectives whose value does not change with each search
operation. An extreme case of this occurs when handling the
objective of generating a temporal plan with short
makespan. Typically used heuristic search methods assume
strictly positive edge costs for their guarantees on
completeness and optimality to hold, while the usual
"fattening" and "advance time" steps of heuristic search
algorithms for temporal planning have the potential for
zero-cost edges, resulting in "g-value plateaus". In this
paper we point out some underlying difficulties with using
modern heuristic search methods for optimizing makespan and
discuss how the presence of these problems contributes to the
poor performance of makespan-optimizing heuristic search
planners. To further illustrate this, we show empirical
results on recent benchmarks using a planner made with
makespan optimization in mind.
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Robert Mattmüller, Manuela Ortlieb, Malte Helmert and Pascal Bercher.
Pattern Database Heuristics for Fully Observable Nondeterministic Planning.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 105-112.
AAAI Press 2010.
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When planning in an uncertain environment, one is often
interested in finding a contingent plan that prescribes
appropriate actions for all possible states that may be
encountered during the execution of the plan. We consider the
problem of finding strong and strong cyclic plans for fully
observable nondeterministic (FOND) planning problems. The
algorithm we choose is LAO*, an informed explicit state search
algorithm. We investigate the use of pattern database (PDB)
heuristics to guide LAO* towards goal states. To obtain a
fully domain-independent planning system, we use an automatic
pattern selection procedure that performs local search in the
space of pattern collections. The evaluation of our system on
the FOND benchmarks of the Uncertainty Part of the
International Planning Competition 2008 shows that our
approach is competitive with symbolic regression search in
terms of problem coverage and speed.
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Hans-Jörg Peter and Robert Mattmüller.
Component-based Abstraction Refinement for
Timed Controller Synthesis.
In
Theodore P. Baker (ed.),
Proceedings of the 30th IEEE Real-Time Systems Symposium
(RTSS 2009), pp. 364-374.
IEEE Computer Society 2009.
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We present a novel technique for synthesizing controllers for
distributed real-time environments with safety requirements.
Our approach is an abstraction refinement extension to the
on-the-fly algorithm by Cassez et al. from 2005. Based on
partial compositions of some environment components, each
refinement cycle constructs a sound abstraction that can be
used to obtain under- and over-approximations of all valid
controller implementations. This enables (1) early
termination if an implementation does not exist in the
over-approximation, or, if one does exist in the
under-approximation, and (2) pruning of irrelevant moves in
subsequent refinement cycles. In our refinement loop, the
precision of the abstractions incrementally increases and
converges to all specification-critical components.
We implemented our approach in a prototype synthesis tool and
evaluated it on an industrial benchmark. In comparison with
the timed game solver UPPAAL-Tiga, our technique outperforms
the nonincremental approach by an order of magnitude.
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Patrick Eyerich, Robert Mattmüller and Gabriele Röger.
Using the Context-enhanced Additive Heuristic for Temporal and Numeric Planning.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 130-137.
AAAI Press 2009.
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Planning systems for real-world applications need the ability
to handle concurrency and numeric fluents. Nevertheless, the
predominant approach to cope with concurrency followed by the
most successful participants in the latest International
Planning Competitions (IPC) is still to find a sequential plan
that is rescheduled in a post-processing step. We present
Temporal Fast Downward (TFD), a planning system for temporal
problems that is capable of finding low-makespan plans by
performing a heuristic search in a temporal search space. We
show how the context-enhanced additive heuristic can be
successfully used for temporal planning and how it can be
extended to numeric fluents. TFD often produces plans of high
quality and, evaluated according to the rating scheme of the
last IPC, outperforms all state-of-the-art temporal planning
systems.
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Pascal Bercher and Robert Mattmüller.
Solving Non-deterministic Planning Problems with
Pattern Database Heuristics.
In
B. Mertsching, M. Hund and Z. Aziz (eds.),
Proceedings of the 32nd Annual Conference on Artificial
Intelligence (KI 2009), pp. 57-64.
Springer-Verlag 2009.
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Non-determinism arises naturally in many real-world
applications of action planning. Strong plans for this type of
problems can be found using AO* search guided by an
appropriate heuristic function. Most domain-independent
heuristics considered in this context so far are based on the
idea of ignoring delete lists and do not properly take the
non-determinism into account. Therefore, we investigate the
applicability of pattern database (PDB) heuristics to
non-deterministic planning. PDB heuristics have emerged as
rather informative in a deterministic context. Our empirical
results suggest that PDB heuristics can also perform
reasonably well in non-deterministic planning. Additionally,
we present a generalization of the pattern additivity
criterion known from classical planning to the
non-deterministic setting.
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Pascal Bercher and Robert Mattmüller.
A Planning Graph Heuristic for Forward-Chaining Adversarial Planning.
In
Proceedings of the 18th European Conference on
Artificial Intelligence (ECAI
2008), pp. 921-922.
IOS Press 2008.
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In contrast to classical planning, in adversarial planning, the
planning agent has to face an adversary trying to prevent him from reaching
his goals. In this paper, we investigate a forward-chaining approach to
adversarial planning based on the AO* algorithm. The exploration of the
underlying AND/OR graph is guided by a heuristic evaluation function,
inspired by the relaxed planning graph heuristic used in the FF
planner. Unlike FF, our heuristic uses an adversarial planning graph with
distinct proposition and action layers for the protagonist and antagonist.
First results suggest that in certain planning
domains, our approach yields results competitive with the state of the art.
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Malte Helmert and Robert Mattmüller.
Accuracy of Admissible Heuristic Functions in Selected Planning Domains.
In
Proceedings of the 23rd AAAI Conference on Artificial Intelligence
(AAAI 2008), pp. 938-943.
AAAI Press 2008.
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The efficiency of optimal planning algorithms based on heuristic
search crucially depends on the accuracy of the heuristic
function used to guide the search. Often, we are interested in
domain-independent heuristics for planning. In order to assess the
limitations of domain-independent heuristic planning, it appears
interesting to analyse the (in)accuracy of common
domain-independent planning heuristics in the IPC benchmark
domains. For a selection of these domains, we analytically
investigate the accuracy of the h+
heuristic, the hm family of heuristics, and
certain (additive) pattern database heuristics, compared to the
perfect heuristic h*. Whereas
h+ and additive pattern database heuristics
usually return cost estimates proportional to the true cost,
non-additive hm and non-additive
pattern-database heuristics can yield results underestimating
the true cost by arbitrarily large factors.
-
Malte Helmert and Robert Mattmüller.
On the Accuracy of Admissible Heuristic Functions in
Selected Planning Domains.
In
Proceedings of the
ICAPS-2007
Workshop on Heuristics for Domain-independent Planning: Progress,
Ideas, Limitations, Challenges.
2007.
Superseded by the AAAI 2008 paper by the same name.
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The efficiency of optimal planning algorithms based on heuristic
search crucially depends on the accuracy of the heuristic
function used to guide the search. Often, we are interested in
domain-independent heuristics for planning. In assessing the
limitations of domain-independent heuristic planning, it appears
interesting to analyse the (in)accuracy of common
domain-independent planning heuristics in the IPC benchmark
domains. For a selection of these domains, we analytically
investigate the accuracy of the h+
heuristic, the hk family of heuristics, and
certain (additive) pattern database heuristics, compared to the
optimal heuristic h*. Whereas
h+ and additive pattern database heuristics
usually return cost estimates proportional to the true cost,
non-additive hk and non-additive
pattern-database heuristics can yield results underestimating
the true cost by arbitrarily large factors.
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Robert Mattmüller and Jussi Rintanen.
Planning for Temporally Extended Goals as Propositional Satisfiability.
In
Proceedings of the 20th International Joint Conference on Artificial Intelligence
(IJCAI 2007), pp. 1966-1971.
2007.
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(poster; PDF)
(BIB)
Planning for temporally extended goals (TEGs) expressed as formulae of
Linear-time Temporal Logic (LTL) is a proper generalization of classical
planning, not only allowing to specify properties of a goal state but of
the whole plan execution. Additionally, LTL formulae can be used to represent
domain-specific control knowledge to speed up planning. In this paper we
extend SAT-based planning for LTL goals (akin to bounded LTL model-checking
in verification) to partially ordered plans, thus significantly increasing
planning efficiency compared to purely sequential SAT planning. We consider
a very relaxed notion of partial ordering and show how planning for LTL
goals (without the next-time operator) can be translated into a SAT problem
and solved very efficiently. The results extend the practical applicability of
SAT-based planning to a wider class of planning problems. In addition, they
could be applied to solving problems in bounded LTL model-checking more
efficiently.
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Malte Helmert, Robert Mattmüller and Sven Schewe.
Selective Approaches for Solving Weak Games.
In
Proceedings of the Fourth International Symposium on
Automated Technology for Verification and Analysis (ATVA 2006), pp. 200-214.
Springer-Verlag 2006.
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Model-checking alternating-time properties has recently
attracted much interest in the verification of distributed
protocols. While checking the validity of a specification in
alternating-time temporal logic (ATL) against an explicit
model is cheap (linear in the size of the formula and the
model), the problem becomes EXPTIME-hard when symbolic
models are considered. Practical ATL model-checking therefore
often consumes too much computation time to be tractable.
In this paper, we describe a novel approach for ATL
model-checking, which constructs an explicit weak model-checking
game on-the-fly. This game is then evaluated using heuristic
techniques inspired by efficient evaluation algorithms for
and/or-trees.
To show the feasibility of our approach, we compare its
performance to the ATL model-checking system MOCHA on some
practical examples. Using very limited heuristic guidance, we
achieve a significant speedup on these benchmarks.
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Malte Helmert, Robert Mattmüller and Gabriele Röger.
Approximation Properties of Planning Benchmarks.
In
Proceedings of the 17th European Conference on Artificial
Intelligence (ECAI 2006), pp. 585-589.
2006.
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For many classical planning domains, the computational complexity of
non-optimal and optimal planning is known. However, little is known
about the area in between the two extremes of finding some plan
and finding optimal plans. In this contribution, we provide a
complete classification of the propositional domains from the first four
International Planning Competitions with respect to the approximation
classes PO, PTAS, APX, poly-APX, and NPO.
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Jens Claßen, Gabriele Röger, Gerhard Lakemeyer and Bernhard Nebel.
PLATAS – Integrating Planning and the Action Language Golog.
KI – Künstliche Intelligenz 26, pp. 61-67. 2012.
(Authors' preprint. The final publication is available at
www.springerlink.com.).
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Action programming languages like Golog allow to define complex
behaviors for agents on the basis of action representations in terms of
expressive (first-order) logical formalisms, making them suitable for
realistic scenarios of agents with only partial world knowledge. Often
these scenarios include sub-tasks that require sequential planning.
While in principle it is possible to express and execute such planning
sub-tasks directly in Golog, the system can performance-wise not
compete with state-of-the-art planners. In this paper, we report on our
efforts to integrate efficient planning and expressive action
programming in the Platas project. The theoretical foundation is laid
by a mapping between the planning language Pddl and the Situation
Calculus, which is underlying Golog, together with a study of how these
formalisms relate in terms of expressivity. The practical benefit is
demonstrated by an evaluation of embedding a Pddl planner into Golog,
showing a drastic increase in performance while retaining the full
expressiveness of Golog.
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Alexander Kleiner, Bernhard Nebel and V.A. Ziparo.
A Mechanism for Dynamic Ride Sharing based on Parallel Auctions.
In
Proc. of the 22th International Joint Conference on Artificial Intelligence (IJCAI).
2011.
(to appear).
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Car pollution is one of the major causes of green- house emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Ex- isting efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that minimize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important feature when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.
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Matthias Westphal, Stefan Wölfl, Bernhard Nebel and Jochen Renz.
On Qualitative Route Descriptions: Representation and
Computational Complexity.
In
Proceedings of the 22nd International Joint
Conference on Artificial Intelligence
(IJCAI 2011), pp. 1120-1125.
AAAI Press 2011.
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The generation of route descriptions is a fundamental
task of navigation systems. A particular problem in this context
is to identify routes that can easily be described and processed
by users. In this work, we present a framework for representing
route networks with the qualitative information necessary to
evaluate and optimize route descriptions with regard to
ambiguities in them. We identify different agent models that
differ in how agents are assumed to process route descriptions
while navigating through route networks. Further, we analyze the
computational complexity of matching route descriptions and paths
in route networks in dependency of the agent model. Finally we
empirically evaluate the influence of the agent model on the
optimization and the processing of route instructions.
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Antje Krumnack, Leandra Bucher, Jelica Nejasmic, Bernhard Nebel and Markus Knauff.
A model for relational reasoning as verbal reasoning.
Cognitive Systems Research 12 (3-4), pp. 377-392. 2011.
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Cai Zhongjie, Dapeng Zhang and Bernhard Nebel.
Playing Tetris Using Bandit-Based Monte-Carlo Planning.
In
Proceedings of AISB 2011 Symposium: AI and Games (AISB 2011).
2011.
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Tetris is a stochastic, open-ended board game. Existing artificial
Tetris players often use different evaluation functions and plan for
only one or two pieces in advance. In this paper, we developed an
artificial player for Tetris using the bandit-based Monte-Carlo
planning method (UCT).
In Tetris, game states are often revisited. However, UCT does not keep
the information of the game states explored in the previous planning
episodes. We created a method to store such information for our player
in a specially designed database to guide its future planning
process. The planner for Tetris has a high branching factor. To
improve game performance, we created a method to prune the planning
tree and lower the branching factor.
The experiment results show that our player can successfully play
Tetris, and the performance of our player is improved as the number of
the played games increases. The player can defeat a benchmark player
with high probabilities.
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Matthias Westphal, Christian Dornhege, Stefan Wölfl, Marc Gissler and Bernhard Nebel.
Guiding the Generation of Manipulation Plans by Qualitative Spatial Reasoning.
Spatial Cognition & Computation: An Interdisciplinary Journal 11 (1), pp. 75-102. 2011.
(BIB)
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Dapeng Zhang and Bernhard Nebel.
Feature Induction of Linear-Chain Conditional Random Fields - A Study Based on a Simulation.
In
Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011).
2011.
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Conditional Random Fields (CRFs) is a probabilistic framework for labeling sequential data. Several approaches
were developed to automatically induce features for CRFs. They have been successfully applied in
real-world applications, e.g. in natural language processing. The work described in this paper was originally
motivated by processing the sequence data of table soccer games. As labeling such data is very time consuming,
we developed a sequence generator (simulation), which creates an extra phase to explore several basic
issues of the feature induction of linear-chain CRFs. First, we generated data sets with different configurations
of overlapped and conjunct atomic features, and discussed how these factors affect the induction. Then, a
reduction step was integrated into the induction which maintained the prediction accuracy and saved the computational
power. Finally, we developed an approach which consists of a queue of CRFs. The experiments
show that the CRF queue achieves better results on the data sets in all the configurations.
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Dapeng Zhang, Cai Zhongjie and Bernhard Nebel.
Playing Tetris Using Learning by Imitation.
In
Proceedings of the 11th annual European Conference on Simulation and AI in Computer Games (GAMEON 2010).
2010.
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Tetris is a stochastic and open-end board game. Several
artificial players were developed to automatically play Tetris.
These players perform well in single games. In this paper,
we developed a platform based on an open source project for
game competitions among multiple players. We develop an
artificial player employed learning by imitation, which is novel
in Tetris. The imitation tasks of playing Tetris were mapped
to a standard data classification problem. The experiments
showed that the performance of the player can be significantly
improved when our player acquires similar game skills as those
of the imitated human. Our player can play Tetris in diverse
ways by imitating different players, and has chances to defeat
the best-known artificial player in the world. The framework
supports incremental learning because the artificial player can
find stronger players and imitate their skills.
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Kai M. Wurm, Christian Dornhege, Patrick Eyerich, Cyrill Stachniss, Bernhard Nebel and Wolfram Burgard.
Coordinated Exploration with Marsupial Teams of Robots using Temporal Symbolic Planning.
In
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010).
2010.
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The problem of autonomously exploring an environment with a team
of robots received considerable attention in the past. However,
there are relatively few approaches to coordinate teams of
robots that are able to deploy and retrieve other
robots. Efficiently coordinating the exploration with such
marsupial robots requires advanced planning mechanisms that are
able to consider symbolic deployment and retrieval actions. In
this paper, we propose a novel approach for coordinating the
exploration with marsupial robot teams. Our method integrates a
temporal symbolic planner that explicitly considers deployment
and retrieval actions with a traditional cost-based assignment
procedure. Our approach has been implemented and evaluated in
several simulated environments and with varying team sizes. The
results demonstrate that our proposed method is able to
coordinate marsupial teams of robots to efficiently explore
unknown environments.
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Thomas Keller, Patrick Eyerich and Bernhard Nebel.
Task Planning for an Autonomous Service Robot.
In
Rüdiger Dillmann, Jürgen Beyerer, Uwe Hanebeck and Tanja Schultz (eds.),
Proceedings on the 33rd Annual German Conference on Artificial Intelligence (KI 2010), pp. 358-365.
Springer-Verlag 2010.
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In the DESIRE project an autonomous robot capable of performing service tasks in a typical kitchen environment has been developed. The overall system consists of various loosely coupled subcomponents providing particular features like manipulating objects or recognizing and interacting with humans. To bring all these subcomponents together to act as monolithic system, a high-performance planning system has been implemented. In this paper, we present this system’s basic architecture and some advanced extensions necessary to cope with the various challenges arising in dynamic and uncertain environments like those a real world service robot is usually faced with.
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Moritz Göbelbecker, Thomas Keller, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
Coming Up with Good Excuses: What To Do When No Plan Can be Found.
In
Ronen Brafman, Héctor Geffner, Jörg Hoffmann and Henry Kautz (eds.),
Proceedings of the 20th International Conference on Automated Planning and Scheduling
(ICAPS 2010), pp. 81-88.
AAAI Press 2010.
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When using a planner-based agent architecture, many things can
go wrong. First and foremost, an agent might fail to execute
one of the planned actions for some reasons. Even more
annoying, however, is a situation where the agent is
incompetent, i.e., unable to come up with a plan. This
might be due to the fact that there are principal reasons that
prohibit a successful plan or simply because the task's
description is incomplete or incorrect. In either case, an
explanation for such a failure would be very helpful. We will
address this problem and provide a formalization of coming
up with excuses for not being able to find a plan. Based
on that, we will present an algorithm that is able to find
excuses and demonstrate that such excuses can be found in
practical settings in reasonable time.
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Patrick Eyerich, Thomas Keller and Bernhard Nebel.
Combining Action and Motion Planning via Semantic Attachments.
In
Proceedings of the Workshop on Combining Action and Motion Planning at ICAPS 2010
(CAMP 2010), p. 19.
2010.
Extended Abstract.
(PDF)
(BIB)
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Marc Gissler, Christian Dornhege, Bernhard Nebel and Matthias Teschner.
Deformable Proximity Queries and their Application in Mobile Manipulation Planning.
In
Symposium on Visual Computing (ISVC 2009), pp. 79-88.
AAAI Press 2009.
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(BIB)
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Christian Dornhege, Marc Gissler, Matthias Teschner and Bernhard Nebel.
Integrating Symbolic and Geometric Planning for Mobile Manipulation.
In
IEEE International Workshop on Safety, Security and Rescue Robotics
(SSRR 2009).
2009.
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Mobile manipulation requires to solve multiple subproblems.
One is planning in high-dimensional configuration spaces, that we approach in this work.
We decompose the manipulation problem into a symbolic and a geometric part.
The symbolic part is implemented as a classical symbolic planner that
tightly integrates a geometric planner enabling us to efficiently generate correct
plans.
A probabilistic roadmap planner constitutes the geometric part.
During the computation of the roadmap we utilize proximity queries to determine non-colliding configurations and to verify collision-free paths between configurations accurately and efficiently.
We demonstrate experiments in two scenarios, one of these being the manipulator dexterity test scenario that was
used in NIST's response robot evaluation in Disaster City.
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Dapeng Zhang, Cai Zhongjie, Chen Kefei and Bernhard Nebel.
A Game Controller Based on Multiple Sensors.
In
In Proceedings of the Fifth International Conference on Advances in Computer Entertainment Tochnology (ACE 2009).
2009.
Video.
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A digital game is normally controlled by hand. Playing such
a game requires only minimum hand movements. Rather
than being easy and comfortable, this game controller is designed
to be physically taxing for the players. It consists of
several sensors, which makes a game more lively and forces
the users to be more physically active. By using different
mapping methods, one game can be played in several ways.
The statistics gathered from the experiments show that even
though the quality of control on the chosen fighting game is
not as high as with a normal joystick, the developed controller
is still preferred by most of the participants. It induces
much more movement than a normal joystick.
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Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner and Bernhard Nebel.
Semantic Attachments for Domain-Independent Planning Systems.
In
Proceedings of the 19th International Conference on Automated
Planning and Scheduling (ICAPS 2009), pp. 114-121.
AAAI Press 2009.
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Solving real-world problems using symbolic planning often
requires a simplified formulation of the original problem,
since certain subproblems cannot be represented at all or only
in a way leading to inefficiency. For example, manipulation
planning may appear as a subproblem in a robotic planning
context or a packing problem can be part of a logistics
task. In this paper we propose an extension of PDDL for
specifying semantic attachments. This allows the evaluation of
grounded predicates as well as the change of fluents by
externally specified functions. Furthermore, we describe a
general schema of integrating semantic attachments into a
forward-chaining planner and report on our experience of
adding this extension to the planners FF and Temporal Fast
Downward. Finally, we present some preliminary experiments
using semantic attachments.
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Michael Brenner and Bernhard Nebel.
Continual Planning and Acting in Dynamic Multiagent Environments.
Journal of Autonomous Agents
and Multiagent Systems 19 (3), pp. 297-331. 2009.
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In order to behave intelligently, artificial agents must be able
to deliberatively plan their future actions. Unfortunately,
realistic agent environments are usually highly dynamic and only
partially observable, which makes planning computationally hard. For
most practical purposes this rules out planning techniques that
account for all possible contingencies in the planning process.
However, many agent environments permit an alternative approach,
namely continual planning, i.e. the interleaving of planning with
acting and sensing.
This paper presents a new principled approach to continual
planning that describes why and when an agent should switch between
planning and acting. The resulting continual planning algorithm
enables agents to deliberately postpone parts of their planning
process and instead actively gather missing information that is
relevant for the later refinement of the plan. To this end, the
algorithm explictly reasons about the knowledge (or lack thereof) of
an agent and its sensory capabilities. These concepts are modelled
in the planning language MAPL. Since in many environments the major
reason for dynamism is the behaviour of other agents, MAPL can also
model multiagent environments, common knowledge among agents, and
communicative actions between them. For Continual Planning, MAPL
introduces the concept of of assertions, abstract actions that
substitute yet unformed subplans.
To evaluate our continual planning approach empirically we have
developed MAPSIM, a simulation environment that automatically builds
multiagent simulations from formal MAPL domains. Thus, agents can
not only plan, but also execute their plans, perceive their
environment, and interact with each other. Our experiments show
that, using continual planning techniques, deliberate action planning
can be used efficiently even in complex multiagent environments.
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Bernhard Nebel and Jochen Renz.
A fixed-parameter tractable algorithm for spatio-temporal calendar management.
In
Proceedings of the 21th International Joint Conference
on Artificial Intelligence (IJCAI 2009), pp. 879--884.
AAAI Press 2009.
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Calendar management tools assist users with coordinating their daily life. Different tasks have to be scheduled according to the user preferences. In many cases, tasks are at different locations and travel times have to be considered.
Therefore, these kinds of calendar management problems can be regarded as spatio-temporal optimisation problems and are often variants of traveling salesman problems (TSP) or vehicle routing problems. While standard TSPs require a solution to include all tasks, prize-collecting TSPs are more suited for calendar management problems as they require a solution that optimises the total sum of ``prizes'' we assigned to tasks at different locations. If we now add time windows that limit when tasks can occur, these prize-collecting TSPs with time windows (TW-TSP) are excellent abstractions of spatio-temporal optimisation problems such as calendar management. Due to the inherent complexity of TW-TSPs, the existing literature considers mainly approximation algorithms or special cases.
We present a novel algorithm for TW-TSPs that enables us to find the optimal solution to TW-TSP problems occurring in real-world calendar management applications efficiently. Our algorithm is a fixed-parameter tractable algorithm that depends on the maximal number of tasks that can be re-visited from some other task, a parameter which is small in the application scenario we consider.
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Bernhard Nebel and Stefan Wölfl.
Benchmarking of Qualitative Spatial and Temporal Reasoning Systems.
2009.
AAAI Technical Report SS-09-02.
(AAAI)
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Paul Plöger, Kai Pervölz, Christoph Mies, Patrick Eyerich, Michael Brenner and Bernhard Nebel.
The DESIRE Service Robotics Initiative.
Künstliche Intelligenz 08 (4), pp. 29-32. 2008.
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We present some advanced hardware units and an appropriate
component based SW architecture for DESIRE. As an example we
describe the integration of a enhanced AI task planner which
allows for higher flexibility and dependability during complex
task execution.
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Patrick Eyerich, Michael Brenner and Bernhard Nebel.
On the Complexity of Planning Operator Subsumption.
In
Proceedings of the Eleventh International Conference on
Principles of Knowledge Representation and Reasoning
(KR
2008), pp. 518-527.
AAAI Press 2008.
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(PDF)
(BIB)
Formal action models play a central role in several subfields of
AI because they are used to model application domains, e.g., in
automated planning. However, there are hitherto no automated
methods for relating such domain models to each other, in
particular for checking whether one is a specialization or
generalization of the other. In this paper, we introduce two kinds
of subsumption relations between operators, both of which are
suitable for modeling and verifying hierarchies between actions
and operators: applicability subsumption considers an action to be
more general than another if the latter can be replaced by the
first at each point in each sound sequence of actions; abstraction
subsumption exploits relations between actions from an ontological
point of view. For both kinds of subsumption, we prove complexity
results for verifying operator subsumption in three important
subclasses: The problems are NP-complete when the expressiveness
of the operators is restricted to the well-known basic STRIPS
formalism, Sigma_p_2-complete when we admit boolean logical operators
and undecidable when the full power of the planning language ADL
is permitted.
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Gabriele Röger, Malte Helmert and Bernhard Nebel.
On the Relative Expressiveness of ADL and Golog: The Last
Piece in the Puzzle.
In
Proceedings of the Eleventh International Conference on
Principles of Knowledge Representation and Reasoning
(KR
2008), pp. 544-550.
AAAI Press 2008.
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(PDF)
Integrating agent programming languages and efficient action
planning is a promising approach because it combines the
expressive power of languages such as Golog with the possibility
of searching for plans efficiently. In order to integrate a
Golog interpreter with a planner, one has to understand,
however, which part of the expressiveness of Golog can be
captured by the planning language. Using Nebel's compilation
framework, we identify a maximal fragment of basic action
theories, the formalism Golog is based on, that is
expressively equivalent to the ADL subset of PDDL. As we will
show, almost all features that permit to specify incomplete
information in basic action theories cannot be compiled to ADL.
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Jussi Rintanen, Bernhard Nebel, J. Christopher Beck and Eric Hansen (eds.).
Proceedings of the 18th International Conference on Automated
Planning and Scheduling
(ICAPS 2008).
AAAI Press, Menlo Park, California USA 2008.
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Thilo Weigel and Bernhard Nebel.
Tischfußball: Mensch versus Computer.
Informatik Spektrum 31, pp. 323-332. 2008.
(Show abstract)
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Table soccer is much simpler than real soccer. Nevertheless, one
faces the same challenges as in all other robotics domains. Sensors
are noisy, actions must be selected under time pressure and the
execution of actions is often less than perfect.
KiRo and StarKick are two systems that have been built to play this
game against humans. These systems are interesting because they are
the first computerized physical games that are played on a level
competitive with experienced humans. Furthermore, these systems enable
us to evaluate different AI techniques in context, e.g., action
selection methods, as is shown in the paper.
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Diedrich Wolter, Frank Dylla, Stefan Wölfl, Jan Oliver Wallgrün, Lutz Frommberger, Bernhard Nebel and Christian Freksa.
SailAway: Spatial Cognition in Sea Navigation.
Künstliche Intelligenz 08 (1), pp. 28-30. 2008.
(DBLP)
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Frank Dylla, Diedrich Wolter, Lutz Frommberger, Christian
Freksa, Stefan Wölfl and Bernhard Nebel.
Qualitative Methoden zur Steuerung von Agenten - SailAway:
Raumkognition zur Steuerung von Schiffen.
Industrie Management 4. 2008.
(BIB)
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Sebastian Kupferschmid, Martin Wehrle, Bernhard Nebel and Andreas Podelski.
Faster than Uppaal?
In
A. Gupta and S. Malik (eds.),
Proceedings of the 20th International Conference on Computer Aided
Verification (CAV 2008), pp. 552-555.
Springer-Verlag 2008.
(Show abstract)
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(PDF)
(BIB)
It is probably very hard to develop a new model checker that
is faster than Uppaal for verifying correct timed automata. In
fact, our tool Mcta does not even try to compete with Uppaal
in this (i.e., Uppaal's) arena. Instead, Mcta is geared
towards analyzing incorrect specifications of timed automata.
It returns (shorter) error traces faster.
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Dapeng Zhang, Bernhard Nebel and Armin Hornung.
Switching Attention Learning - A Paradigm for Introspection and Incremental Learning.
In
Proceedings of Fifth International Conference on Computational Intelligence, Robotics
and Autonomous Systems (CIRAS 2008), pp. 99-104.
Linz, Austria 2008.
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(PDF)
(PS.GZ)
Humans improve their sport skills by eliminating one recognizable
weakness at a time. Inspired by this observation, we
define a learning paradigm in which different learners can
be plugged together. An extra attention model is in charge
of iterating over them and chosing which one to improve
next. The paradigm is named Switching Attention Learning
(SAL). The essential idea is that improving one model in the
system generates more "improvement space" for the others.
Using SAL, an application for tracking the game ball in a
table soccer game-recorder is implemented. We developed
several models and learners which work together in the SAL
framework, producing satisfying results in the experiments.
The related problems, advantages, and perspective of the
switching attention learning are discussed in this paper.
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Jochen Renz and Bernhard Nebel.
Qualitative Spatial Reasoning using Constraint Calculi.
In
M. Aiello, I. Pratt-Hartmann and J. van Benthem (eds.),
Handbook of Spatial Logics, pp. 161-215.
Springer-Verlag 2007.
-
Dapeng Zhang and Bernhard Nebel.
Recording and Segmenting Table Soccer Games -- Initial Results.
In
Proceedings of the 1st International Symposium on Skill Science 2007
(ISSS
2007), pp. 193-195.
2007.
Poster.
(Show abstract)
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(PDF)
(PS.GZ)
Robot KiRo can play one side of a table soccer game autonomously.
Our recent research focuses on learning from and acting against
human actions. Therefore recording and segmenting games played by
humans are motivated. In this paper, the construction of a table
soccer game recorder is sketched. An intuitive segmenting
algorithm is implemented to explore the properties of the recorded
data. A segmentation approach using Hidden Markov Models (HMMs) is
proposed.
-
Dapeng Zhang and Bernhard Nebel.
Learning a Table Soccer Robot a New Action Sequence by Observing and Imitating.
In
Proceedings of the Third Artificial Intelligence for
Interactive Digital Entertainment Conference (AIIDE
2007), pp. 61-67.
2007.
Experiment Video.
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(PS.GZ)
Star-Kick is a commercially available and fully automatic
table soccer (foosball) robot, which plays table
soccer games against human players on a competitive
level. One of our research goals is to learn this table
soccer robot skillful actions similar to a human player
based on a moderate number of trials. Two independent
learning algorithms are employed for learning a
new lock and slide-kick action sequence by observing
the performed actions and imitating the relative actions
of a human player. The experiments with Star-Kick
show that an effective action sequence can be learned
in approximately 20 trials.
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Diedrich Wolter, Frank Dylla, Lutz Frommberger, Jan Oliver Wallgrün, Bernhard Nebel and Stefan Wölfl.
Qualitative Spatial Reasoning for Rule Compliant Agent Navigation.
In
Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2007), pp. 673-674.
AAAI Press 2007.
(DBLP)
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Gabriele Röger and Bernhard Nebel.
Expressiveness of ADL and Golog:
Functions Make a Difference.
In
Proceedings of the 22nd AAAI Conference on Artificial
Intelligence (AAAI 2007), pp. 1051-1056.
AAAI Press 2007.
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The main focus in the area of action languages, such as
GOLOG, was put on expressive power, while the development
in the area of action planning was focused on efficient
plan generation. An integration of GOLOG and planning languages
would provide great advantages. A user could constrain
a systems behavior on a high level using GOLOG,
while the actual low-level actions are planned by an efficient
planning system. First endeavors have been made by Eyerich
et al. by identifying a subset of the situation calculus (which
is the basis of GOLOG) with the same expressiveness as the
ADL fragment of PDDL. However, it was not proven that the
identified restrictions define a maximum subset. The most
severe restriction appears to be that functions are limited to
constants. We will show that this restriction is indeed necessary
in most cases.
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Vittorio Ziparo, Alexander Kleiner, Bernhard Nebel and Daniele Nardi.
RFID-Based Exploration for Large Robot Teams.
In
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2007), pp. 4606-4613.
Rome, Italy 2007.
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Sanjiang Li and Bernhard Nebel.
Qualitative spatial representation and reasoning: A Hierarchical approach.
The Computer Journal, pp. 391-402. 2007.
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The ability to reason in space is crucial for agents in order to make
informed decisions. Current high-level qualitative approaches to spatial
reasoning has serious decisionsciencies in not recting the hierarchical nature of spatial data and human spatial cognition. This paper proposes a
framework for hierarchical representation and reasoning about topological information, where a continuous model of space is approximated by
a collection of discrete sub-models, and spatial information is hierarchically represented in discrete sub-models in a rough set manner. The work
is based on the GRCC theory, where continuous and discrete models of
space are coped in a uni-ulmed way. Reasoning issues such as determining
the mereological (part-whole) relations between two rough regions are also
discussed. Moreover, we consider an important problem that is closely related to map generalization in cartography and Geographical Information
Science. Given a spatial considerguration at a spatialner level, we show how to construct a configuration at a coarser level while preserving the mereological
relations.
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Jens Claßen, Patrick Eyerich, Gerhard Lakemeyer and Bernhard Nebel.
Towards an Integration of Golog and Planning.
In
Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 1846-1851.
AAAI Press 2007.
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The action language Golog has been applied successfully
to the control of robots, among other
things. Perhaps its greatest advantage is that a
user can write programs which constrain the search
for an executable plan in a xible manner. However,
when general planning is needed, Golog supports
this only in principle, but does not measure
up with state-of-the-art planners. In this paper we
propose an integration of Golog and planning in the
sense that planning problems, formulated as part of
a Golog program, are solved by a modern planner
during the execution of the program. Here we focus
on the ADL subset of the plan language PDDL.
First we show that the semantics of ADL can be
understood as progression in the situation calculus,
which underlies Golog, thus providing us with a
correct embedding of ADL within Golog. We then
show how Golog can be integrated with an existing
ADL planner for closed-world initial databases and
compare the performance of the resulting system
with the original Golog.
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Patrick Eyerich, Bernhard Nebel, Gerhard Lakemeyer and Jens Classen.
Golog and PDDL: What is the Relative Expressiveness?
In
Proceedings of the International Symposium on Practical Cognitive Agents and Robots (PCAR 2006), pp. 93-104.
University of Western Australia Press 2006.
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Action formalisms such as GOLOG or FLUX have been developed
primarily for representing and reasoning about change in a logical framework.
For this reason, expressivity was the main goal in the development of these formalisms.
In another line of research, efficiency of planning methods was the topmost
goal resulting in the basic STRIPS language, which has only moderate expressivity.
The planning language PDDL developed since 1998 is an extension
of basic STRIPS with many expressive features. Now the interesting question is
how PDDL compares to GOLOG or other action languages from an expressivity
point of view. We will show that a GOLOG fragment, which we call Restricted
Basic Action Theories, is as expressive as the ADL fragment of PDDL. To prove
this equivalence we use the compilation framework. From a practical point of
view, this result can be used for employing efficient planners inside a GOLOG
interpreter.
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Michael Brenner and Bernhard Nebel.
Continual Planning and Acting in Dynamic Multiagent Environments.
In
Proceedings of the International Symposium on Practical Cognitive Agents and Robots.
Perth, Australia 2006.
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Jona Boeddinghaus, Marco Ragni, Markus Knauff and Bernhard Nebel.
Simulating spatial reasoning using ACT-R.
In
Proceedings of the Seventh International Conference on Cognitive Modeling
(ICCM 2006).
2006.
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We present an ACT-R model of spatial reasoning based on
the SRM model (Spatial Reasoning by Models). This model
maps spatial working memory to a two-dimensional array and
uses a spatial focus to place objects in the array, manipulate
the position of objects, and inspect the array to find spatial
relations that are not given in the premises. Since the SRM
explains many experimental findings only on a qualitative
level, we implemented it into an ACT-R model. Not only does
the model show some well-known effects in spatial reasoning
and offers a good insight into the processes in the SRM
model, but in addition it also allows us to predict reasoning
times. The Model is accessible through a Java interface,
which can be found and run from the following website
http://www.informatik.uni-freiburg.de/~srm.
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Alexander Kleiner, Johann Prediger and Bernhard Nebel.
RFID Technology-based Exploration and SLAM for Search And Rescue.
In
Proceedings of the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2006), pp. 4054-4059.
Beijing, China 2006.
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Robot search and rescue is a time critical task, i.e.
a large terrain has to be explored by multiple robots within
a short amount of time. The efficiency of exploration depends
mainly on the coordination between the robots and hence on the
reliability of communication, which considerably suffers under
the hostile conditions encountered after a disaster. Furthermore,
rescue robots have to generate a map of the environment which
has to be sufficiently accurate for reporting the locations of
victims to human task forces. Basically, the robots have to
solve autonomously in real-time the problem of Simultaneous
Localization and Mapping (SLAM).
This paper proposes a novel method for real-time exploration
and SLAM based on RFID tags that are autonomously distributed
in the environment. We utilized the algorithm of Lu
and Milios [8] for calculating globally consistent maps from
detected RFID tags. Furthermore we show how RFID tags can
be used for coordinating the exploration of multiple robots.
Results from experiments conducted in the simulation and
on a robot show that our approach allows the computationally
efficient construction of a map within harsh environments, and
coordinated exploration of a team of robots.
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Alexander Kleiner, Christian Dornhege, Rainer Kuemmerle, Michael Ruhnke, Bastian Steder, Bernhard Nebel, Patrick Doherty, Mariusz Wzorek, Piotr Rudol, Gianpaolo Conte, S. Durante and D. Lundstrom.
RoboCupRescue - Robot League Team RescueRobots Freiburg (Germany), Team Description Paper.
In
CDROM Proceedings of the International RoboCup Symposium '05.
Bremen, Germany 2006.
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This paper describes the approach of the RescueRobots Freiburg team,
which is a team of students from the University of Freiburg that originates from
the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team
(RoboCupRescue Simulation). Furthermore we introduce linkMAV, a micro aerial
vehicle platform.
Our approach covers RFID-based SLAM and exploration, autonomous detection
of relevant 3D structures, visual odometry, and autonomous victim identification.
Furthermore, we introduce a custom made 3D Laser Range Finder (LRF) and a
novel mechanism for the active distribution of RFID tags.
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Marco Ragni, Markus Knauff and Bernhard Nebel.
A Computational Model for Spatial Reasoning with Mental Models.
In
Proceedings of the 27th Annual Cognitive Science Conference (CogSci-05).
2005.
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We propose a computational model for spatial reasoning by
means of mental models. Our SRM model (Spatial Reasoning
by Models) maps spatial working memory to a twodimensional
array and uses a spatial focus that places objects
in the array, manipulates the position of objects, and inspects
the array to find spatial relations that are not given in the
premises. The SRM model results in a computational
complexity measure that relies on the number of operations in
the array and the number of relations that must be handled.
The performance of the SRM model is compared to the
performance of human subjects reported in the literature and
in our own study.
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Sylvie Thiebaux, Jörg Hoffmann and Bernhard Nebel.
In Defense of Axioms in PDDL.
Artificial Intelligence 168 (1-2), pp. 38-69. 2005.
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There is controversy as to whether explicit support for PDDL-like axioms and derived predicates
is needed for
