Institut für Informatik

Institut für Informatik | Technische Fakultät | Universität Freiburg

Arbeitsgruppe Grundlagen der Künstlichen Intelligenz

Thomas Keller – Publikationen

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2009

Christian Dornhege, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner und Bernhard Nebel.
Semantic Attachments for Domain-Independent Planning Systems.
In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS 2009), S. 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.

2008

Thomas Keller.
Optimales domänenspezifisches Planen in der Transport- und Routenplanungsfamilie.
Diplomarbeit, Albert-Ludwigs-Universität, Freiburg, Germany 2008.
In German.
(PDF)
Thomas Keller und Sebastian Kupferschmid.
Automatic Bidding for the Game of Skat.
In Andreas R. Dengel, Karsten Berns, Thomas M. Breuel, Frank Bomarius und Thomas R. Roth-Berghofer, Proceedings of the 31st Annual German Conference on AI (KI 2008), S. 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.