Armin Hornung - Publications

Publications

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2012

Armin Hornung and Maren Bennewitz.
Adaptive Level-of-Detail Planning for Efficient Humanoid Navigation.
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2012. To appear.
(Show BibTeX)

@INPROCEEDINGS{hornung12icra,
  author = {Armin Hornung and Maren Bennewitz},
  title = {Adaptive Level-of-Detail Planning for Efficient Humanoid Navigation},
  booktitle = {Proc.~of the IEEE International Conference on Robotics and 
  Automation (ICRA)},
  year = {2012},
  month = {May},
  address = {St. Paul, MN, USA},
  note = {To appear}
}

Armin Hornung, Mike Phillips, E. Gil Jones, Maren Bennewitz, Maxim Likhachev, and Sachin Chitta.
Navigation in Three-Dimensional Cluttered Environments for Mobile Manipulation.
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2012. To appear.
(Show BibTeX)

@INPROCEEDINGS{hornung12icra_pr2,
  author = {Armin Hornung and Mike Phillips and E. Gil Jones and 
  Maren Bennewitz and Maxim Likhachev and Sachin Chitta},
  title = {Navigation in Three-Dimensional Cluttered Environments 
  for Mobile Manipulation},
  booktitle = {Proc.~of the IEEE International Conference on Robotics and 
  Automation (ICRA)},
  year = {2012},
  month = {May},
  address = {St. Paul, MN, USA},
  note = {To appear}
}

2011

Maren Bennewitz, Daniel Maier, Armin Hornung, and Cyrill Stachniss.
Integrated Perception and Navigation in Complex Indoor Environments.
In: Proceedings of the HUMANOIDS 2011 workshop on Humanoid service robot navigation in crowded and dynamic environments, 2011.
(Show BibTeX)

@INPROCEEDINGS{bennewitz11humws,
  author =   {Maren Bennewitz and Daniel Maier and Armin Hornung and 
  Cyrill Stachniss},
  title = {Integrated Perception and Navigation in Complex Indoor Environments},
  booktitle = {Proc. of the HUMANOIDS 2011 workshop on Humanoid service 
  robot navigation in crowded and dynamic environments},
  year = 2011,
  month = {October},
  address = {Bled, Slovenia}
}

Stefan Oßwald, Jens-Steffen Gutmann, Armin Hornung, and Maren Bennewitz.
From 3D Point Clouds to Climbing Stairs: A Comparison of Plane Segmentation Approaches for Humanoids.
In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), 2011.
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In this paper, we consider the problem of building 3D models of complex staircases based on laser range data acquired with a humanoid. These models have to be sufficiently accurate to enable the robot to reliably climb up the staircase. We evaluate two state-of-the-art approaches to plane segmentation for humanoid navigation given 3D range data about the environment. The first approach initially extracts line segments from neighboring 2D~scan lines, which are successively combined if they lie on the same plane. The second approach estimates the main directions in the environment by randomly sampling points and applying a clustering technique afterwards to find planes orthogonal to the main directions. We propose extensions for this basic approach to increase the robustness in complex environments which may contain a large number of different planes and clutter. In practical experiments, we thoroughly evaluate all methods using data acquired with a laser-equipped Nao robot in a multi-level environment. As the experimental results show, the reconstructed 3D models can be used to autonomously climb up complex staircases.
```
@INPROCEEDINGS{osswald11humanoids,
  author = {Stefan O{\ss}wald and Jens-Steffen Gutmann and Aarmin Hornung 
  and Maren Bennewitz},
  title = {From 3{D} Point Clouds to Climbing Stairs: A Comparison of Plane 
  Segmentation Approaches for Humanoids},
  booktitle = {Proc.~of the IEEE-RAS International Conference 
  on Humanoid Robots (Humanoids)},
  year = 2011,
  month = {October},
  address = {Bled, Slovenia}
}    
```

Armin Hornung, E. Gil Jones, Sachin Chitta, Maren Bennewitz, Mike Phillips, and Maxim Likhachev.
Towards Navigation in Three-Dimensional Cluttered Environments.
In: Abstract Proceedings of The PR2 Workshop: Results, Challenges and Lessons Learned in Advancing Robots with a Common Platform at IROS 2011.
(Show BibTeX)

@INPROCEEDINGS{hornung11irosws,
  author = {A. Hornung and E. G. Jones and S. Chitta and  M. Bennewitz and 
  M. Phillips and M. Likhachev},
  title = {Towards Navigation in Three-Dimensional Cluttered Environments},
  booktitle = {Proc. of the IROS 2011 PR2 Workshop: Results, Challenges and 
  Lessons Learned in Advancing Robots with a Common Platform },
  year = 2011,
  month = {September},
  address = {San Francisco, CA, USA}  
}

Stefan Oßwald, Attila Görög, Armin Hornung, and Maren Bennewitz.
Autonomous Climbing of Spiral Staircases with Humanoids.
In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011.
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In this paper, we present an approach to enable a humanoid robot to autonomously climb up spiral staircases. This task is substantially more challenging than climbing straight stairs since careful repositioning is needed. Our system globally estimates the pose of the robot, which is subsequently refined by integrating visual observations. In this way, the robot can accurately determine its relative position with respect to the next step. We use a 3D~model of the environment to project edges corresponding to stair contours into monocular camera images. By detecting edges in the images and associating them to projected model edges, the robot is able to accurately locate itself towards the stairs and to climb them. We present experiments carried out with a Nao humanoid equipped with a 2D~laser range finder for global localization and a low-cost monocular camera for short-range sensing. As we show in the experiments, the robot reliably climbs up the steps of a spiral staircase.
```
@INPROCEEDINGS{osswald11iros,
  author = {Stefan O{\ss}wald and Attila G{\"o}r{\"o}g 
   and Armin Hornung and Maren Bennewitz},
  title = {Autonomous Climbing of Spiral Staircases with Humanoids},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on
  Intelligent Robots and Systems (IROS)},
  year = 2011,
  month = {September},
  address = {San Francisco, CA, USA}
}
```
Johannes Garimort, Armin Hornung, and Maren Bennewitz.
Humanoid Navigation with Dynamic Footstep Plans.
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2011.
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Humanoid robots possess the capability of stepping over or onto objects, which distinguishes them from wheeled robots. When planning paths for humanoids, one therefore should consider an intelligent placement of footsteps instead of choosing detours around obstacles. In this paper, we present an approach to optimal footstep planning for humanoid robots. Since changes in the environment may appear and a humanoid may deviate from its originally planned path due to imprecise motion execution or slippage on the ground, the robot might be forced to dynamically revise its plans. Thus, efficient methods for planning and replanning are needed to quickly adapt the footstep paths to new situations. We formulate the problem of footstep planning so that it can be solved with the incremental heuristic search method D* Lite and present our extensions, including continuous footstep locations and efficient collision checking for footsteps. In experiments in simulation and with a real Nao humanoid, we demonstrate the effectiveness of the footstep plans computed and revised by our method. Additionally, we evaluate different footstep sets and heuristics to identify the ones leading to the best performance in terms of path quality and planning time. Our D* Lite algorithm for footstep planning is available as open source implementation.
```
@INPROCEEDINGS{garimort11icra,
  author = {Johannes Garimort and Armin Hornung and Maren Bennewitz},
  title = {Humanoid Navigation with Dynamic Footstep Plans},
  booktitle = {Proc.~of the IEEE International Conference on Robotics and 
  Automation (ICRA)},
  year = 2011,
  month = {May},
  address = {Shanghai, China}
}
```

2010

Armin Hornung, Kai M. Wurm, and Maren Bennewitz.
Humanoid Robot Localization in Complex Indoor Environments.
In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010.
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In this paper, we present a localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing. Reliable and accurate localization for humanoid robots operating in such environments is a challenging task. First, humanoids typically execute motion commands rather inaccurately and odometry can be estimated only very roughly. Second, the observations of the small and lightweight sensors of most humanoids are seriously affected by noise. Third, since most humanoids walk with a swaying motion and can freely move in the environment, e.g., they are not forced to walk on flat ground only, a 6D torso pose has to be estimated. We apply Monte Carlo localization to globally determine and track a humanoid's 6D pose in a 3D world model, which may contain multiple levels connected by staircases. To achieve a robust localization while walking and climbing stairs, we integrate 2D laser range measurements as well as attitude data and information from the joint encoders. We present simulated as well as real-world experiments with our humanoid and thoroughly evaluate our approach. As the experiments illustrate, the robot is able to globally localize itself and accurately track its 6D pose over time.
```
@INPROCEEDINGS{hornung10iros,
  author = {Armin Hornung and Kai M. Wurm and Maren Bennewitz},
  title = {Humanoid Robot Localization in Complex Indoor Environments},
  booktitle =  {Proc.~of the IEEE/RSJ International Conference on Intelligent 
  Robots and Systems (IROS)},
  year = 2010,
  address = {Taipei, Taiwan},
  month= {October}
}
```
Armin Hornung, Maren Bennewitz, and Wolfram Burgard.
Learning Efficient Vision-based Navigation.
In: Abstract Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2010.
Armin Hornung, Maren Bennewitz, Cyrill Stachniss, Hauke Strasdat, Stefan Oßwald, and Wolfram Burgard.
Learning Adaptive Navigation Strategies for Resource-Constrained Systems.
In: Proceedings of the 3rd International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems (ERLARS) at the European Conference on Artificial Intelligence, 2010.
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The majority of navigation algorithms for mobile robots assume that the robots possess enough computational or memory resources to carry out the necessary calculations. Especially small and lightweight devices, however, are resource-constrained and have only restricted capabilities. In this paper, we present a reinforcement learning approach for mobile robots that considers the imposed constraints on their sensing capabilities and computational resources, so that they can reliably and efficiently fulfill their navigation tasks. Our technique learns a policy that optimally trades off the speed of the robot and the uncertainty in the observations imposed by its movements. It furthermore enables the robot to learn an efficient landmark selection strategy to compactly model the environment. We describe extensive simulated and real-world experiments carried out with both wheeled and humanoid robots which demonstrate that our learned navigation policies significantly outperform strategies using advanced and manually optimized heuristics.
```
@INPROCEEDINGS{hornung10erlars,
  author = {Armin Hornung and Maren Bennewitz and Cyrill Stachniss and 
  Hauke Strasdat and Stefan O{\ss}wald and Wolfram Burgard},
  title = {Learning Adaptive Navigation Strategies for Resource-constrained 
  Systems},
  booktitle = {Proc.~of the 3rd Int.~Workshop on Evolutionary and Reinforcement 
  Learning for Autonomous Robot Systems (ERLARS)},
  address = {Lisbon, Portugal},
  year = 2010,
  month = {August}
}
```
Armin Hornung and Maren Bennewitz.
Robust and Adaptive Navigation with Humanoid Robots.
In: Abstract Proceedings of the Workshop on Motion Planning: From Theory to Practice at Robotics: Science and Systems (RSS), 2010.
Armin Hornung, Maren Bennewitz, and Hauke Strasdat.
Efficient Vision-based Navigation - Learning about the Influence of Motion Blur.
In: Autonomous Robots, Vol. 29, Number 2, 2010.
(Show abstract) (Hide abstract) (Show BibTeX) (Hide BibTeX) PDF Video DOI: 10.1007/s10514-010-9190-3

In this article, we present a novel approach to learning efficient navigation policies for mobile robots that use visual features for localization. As fast movements of a mobile robot typically introduce inherent motion blur in the acquired images, the uncertainty of the robot about its pose increases in such situations. As a result, it cannot be ensured anymore that a navigation task can be executed efficiently since the robot's pose estimate might not correspond to its true location. We present a reinforcement learning approach to determine a navigation policy to reach the destination reliably and, at the same time, as fast as possible. Using our technique, the robot learns to trade off velocity against localization accuracy and implicitly takes the impact of motion blur on observations into account. We furthermore developed a method to compress the learned policy via a clustering approach. In this way, the size of the policy representation is significantly reduced, which is especially desirable in the context of memory-constrained systems. Extensive simulated and real-world experiments carried out with two different robots demonstrate that our learned policy significantly outperforms policies using a constant velocity and more advanced heuristics. We furthermore show that the policy is generally applicable to different indoor and outdoor scenarios with varying landmark densities as well as to navigation tasks of different complexity.
```
@ARTICLE{hornung10ar,
  author = {Armin Hornung and Maren Bennewitz and Hauke Strasdat},
  title = {Efficient Vision-based Navigation -- {L}earning about the Influence 
  of Motion Blur},
  journal = {Autonomous Robots},
  year = 2010,
  volume = 29,
  issue = 2,
  pages = {137-149},
  doi = {http://dx.doi.org/10.1007/s10514-010-9190-3}
}
```
Kai M. Wurm, Armin Hornung, Maren Bennewitz, Cyrill Stachniss, and Wolfram Burgard.
OctoMap: A Probabilistic, Flexible, and Compact 3D Map Representation for Robotic Systems.
In: Proceedings of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation, 2010.
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In this paper, we present an approach for modeling 3D environments based on octrees using a probabilistic occupancy estimation. Our technique is able to represent full 3D models including free and unknown areas. It is available as an open-source library to facilitate the development of 3D mapping systems. We also provide a detailed review of existing approaches to 3D modeling. Our approach was thoroughly evaluated using different real-world and simulated datasets. The results demonstrate that our approach is able to model the data probabilistically while, at the same time, keeping the memory requirement at a minimum.
```
@inproceedings{wurm10icraws,
author = {K. M. Wurm and A. Hornung and 
M. Bennewitz and C. Stachniss and W. Burgard},
title = {{OctoMap}: A Probabilistic, Flexible, and Compact {3D} Map 
Representation for Robotic Systems},
booktitle = {Proc. of the ICRA 2010 Workshop on Best Practice in 
3D Perception and Modeling for Mobile Manipulation},
year = 2010,
month = may,
address = {Anchorage, AK, USA},
url = {http://octomap.sf.net/},
note = {Software available at \url{http://octomap.sf.net/}}
}
```
Stefan Oßwald, Armin Hornung, and Maren Bennewitz.
Learning Reliable and Efficient Navigation with a Humanoid.
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2010.
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Reliable and efficient navigation with a humanoid robot is a difficult task. First, the motion commands are executed rather inaccurately due to backlash in the joints or foot slippage. Second, the observations are typically highly affected by noise due to the shaking behavior of the robot. Thus, the localization performance is typically reduced while the robot moves and the uncertainty about its pose increases. As a result, the reliable and efficient execution of a navigation task cannot be ensured anymore since the robot's pose estimate might not correspond to the true location. In this paper, we present a reinforcement learning approach to select appropriate navigation actions for a humanoid robot equipped with a camera for localization. The robot learns to reach the destination reliably and as fast as possible, thereby choosing actions to account for motion drift and trading off velocity in terms of fast walking movements against accuracy in localization. We present extensive simulated and practical experiments with a humanoid robot and demonstrate that our learned policy significantly outperforms a hand-optimized navigation strategy.
```
@INPROCEEDINGS{osswald10icra,
  author = {Stefan O{\ss}wald and Armin Hornung and Maren Bennewitz},
  title = {Learning Reliable and Efficient Navigation with a Humanoid},
  booktitle = {Proc.~of the IEEE International Conference on Robotics and 
  Automation (ICRA)},
  year = {2010},
  address = {Anchorage, AK, USA},
  month = {May}
}
```

2009

Armin Hornung, Hauke Strasdat, Maren Bennewitz, and Wolfram Burgard.
Learning Efficient Policies for Vision-based Navigation.
In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009.
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Cameras are popular sensors for robot navigation tasks such as localization as they are inexpensive, lightweight, and provide rich data. However, fast movements of a mobile robot typically reduce the performance of vision-based localization systems due to motion blur. In this paper, we present a reinforcement learning approach to choose appropriate velocity profiles for vision-based navigation. The learned policy minimizes the time to reach the destination and implicitly takes the impact of motion blur on observations into account. To reduce the size of the resulting policies, which is desirable in the context of memory-constrained systems, we compress the learned policy via a clustering approach. Extensive simulated and real-world experiments demonstrate that our learned policy significantly outperforms any policy that uses a constant velocity. We furthermore show, that our policy is applicable to different environments. Additional experiments demonstrate that our compressed policies do not result in a performance loss compared to the originally learned policy.
```
@INPROCEEDINGS{hornung09iros,
  author = {Armin Hornung and Hauke Strasdat and Maren Bennewitz and Wolfram
  Burgard},
  title = {Learning Efficient Policies for Vision-based Navigation},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent 
  Robots and Systems (IROS)},
  year = {2009},
  address = {St. Louis, MO, USA},
  month = {October}
}
```
Armin Hornung.
Learning Policies for Reliable Mobile Robot Localization.
Diploma thesis, Albert-Ludwigs-Universität, Freiburg, Germany 2009.
Awarded with the VDI-Förderpreis by the Association of German Engineers
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Cameras are a useful sensor for mobile robot localization because they are relatively cheap, compact, and lightweight. This makes them attractive for robots with payload limitations, such as humanoid robots or small unmanned aerial vehicles. However, fast movements typically reduce the ability to use a vision-based localization due to motion blur.

In this thesis, we present a reinforcement learning approach for a robot learning a vision-based navigation policy. The learned policy minimizes the time to reach the destination and implicitly takes the impact of motion blur on landmark observations and thus on localization into account. Extensive simulated and real-world experiments show that our learned policy significantly outperforms any policy of moving with a constant velocity and is generally applicable to different environments. We experimentally determined the most relevant state features for the learning task. Additionally, we present a method for compressing the learned policy with a clustering approach. While the size of the policy representation is drastically reduced, our experiments show that there is no loss of performance. This is especially valuable for memory-constrained systems.
```
@MASTERSTHESIS{hornung09diplom,
  author = {Armin Hornung},
  title = {Learning Policies for Reliable Mobile Robot Localization},
  school = {Albert-Ludwigs-Universit{\"a}t Freiburg},
  year = {2009},
  type = {Diplomarbeit},
  address = {Freiburg, Germany},
  month = {January}
}
```

2008

Dapeng Zhang and Armin Hornung.
A Table Soccer Game Recorder.
In: Video Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2008.
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Our table soccer robot can already challenge even professional human players. Next, the robot should play games by using human-like skills. As a foundation of this research, our table soccer game recorder can save and replay games played by humans. This video shows the construction and functionality of the recording system. We use three types of sensors mounted on a regular game table. The movement of a game rod is measured by an optical distance sensor. Its turning is observed by a magnetic rotary encoder. Two laser measurement systems are synchronized to determine the position of the ball. The raw sensor data is smoothed by an approach using multi-model Kalman filter. We developed several software modules for the system. The modules provide a basis for the future research.
```
@INPROCEEDINGS{zhang08iros,
  author = {Zhang, Dapeng and Hornung, Armin},
  title = {A Table Soccer Game Recorder},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent 
  Robots and Systems (IROS)},
  year = {2008},
  address = {Nice, France},
  month = {September}
}
```
Armin Hornung and Dapeng Zhang.
On-line Detection of Rule Violations in Table Soccer.
In KI 2008: Advances in Artificial Intelligence (KI 2008), pp. 217-224. Springer Verlag Berlin/Heidelberg; Kaiserslautern, Germany 2008.
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In table soccer, humans can not always thoroughly observe fast actions like rod spins and kicks. However, this is necessary in order to detect rule violations for example for tournament play. We describe an automatic system using sensors on a regular soccer table to detect rule violations in realtime. Naive Bayes is used for kick classification, the parameters are trained using supervised learning. In the on-line experiments, rule violations were detected at a higher rate than by the human players. The implementation proved its usefulness by being used by humans in real games and sets a basis for future research using probability models in table soccer.
```
@INPROCEEDINGS{hornung08ki,
  author = {Hornung, Armin and Zhang, Dapeng},
  title = {On-Line Detection of Rule Violations in Table Soccer},
  booktitle = {KI '08: Proceedings of the 31st annual German conference on 
  Advances in Artificial Intelligence},
  year = {2008},
  pages = {217--224},
  address = {Berlin, Heidelberg},
  month = {March},
  publisher = {Springer-Verlag},
  doi = {http://dx.doi.org/10.1007/978-3-540-85845-4_27},
  isbn = {978-3-540-85844-7},
  location = {Kaiserslautern, Germany}
}
```
Dapeng Zhang, Bernhard Nebel and Armin Hornung.
Switching Attention Learning - A Paradigm for Introspection and Incremental Learning.
In Proceedings of the Fifth International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2008). Linz, Austria 2008.
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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.
```
@INPROCEEDINGS{zhang08ciras,
  author = {Zhang, Dapeng and Nebel, Bernhard and Hornung, Armin},
  title = {Switching Attention Learning -- A Paradigm for Introspection and 
  Incremental Learning},
  booktitle = {Proc.~of the 5th International Conference on Computational 
  Intelligence, Robotics and Autonomous Systems (CIRAS)},
  year = {2008},
  address = {Linz, Austria},
  month = {June}
}
```

2007

Armin Hornung.
Detecting Violations in Table Soccer Games Using Naive Bayes Classifiers.
Student project, Albert-Ludwigs-Universität, Freiburg, Germany 2007.
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In table soccer, humans can not always accurately observe fast actions like rod spins and kicks. However, this is necessary in order to detect rule violations for example for tournament play. This project describes an automatic referee using sensors on a regular soccer table to detect rule violations. The gap between noisy sensor data and the high-level concept of a kick is bridged by using naive Bayes classifiers for kick detection. Input to the classifiers are the coordinates of the ball relative to the kicking figure, modelled as Gaussian distributions. The classifier is trained offline using supervised learning. In the experiments, all rule violations were detected online. The implementation proved its usefulness by being used in regular games. Future work on segmenting table soccer games or sequence learning can benefit from the findings of this project by classifying game situations with the methods described here.