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Seminar: Theorie und Praxis autonomer Systeme - Themen

Nr.	Titel	Vortragender	Betreuer
1.	Komplexität Probabilistischer Planung: Goldsmith, J., Littman, M. L., and Mundhenk, M., The complexity of plan existence and evaluation in probabilistic domains. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), 182--189. San Francisco, CA: Morgan Kaufmann Publishers, 1997 Hintergrundmaterial: Littman, M.L., Goldsmith, J., and Mundhenk, M., The Computational Complexity of Probabilistic Planning, Journal of Artificial Intelligence Research, 9:1-36, 1998.	Abgesagt	Abgesagt
2.	Entscheidungstheoretisches Planen: C. Boutilier, T. Dean, and S. Hanks. Planning under uncertainty: structural assumptions and computational leverage, pages 157--172. In New Directions in AI Planning (ECP-95), IOS Press, Amsterdam, 1996. Hintergrundmaterial: Boutilier, C., Dean, T., and Hanks, S., Decision-Theoretic Planning: Structural Assumptions and Computational Leverage, Journal of Artificial Intelligence Research, 11: 1-94, 1999.	Gabi Röger	Prof. Dr. Bernhard Nebel
3.	Planerkennung und Vorhersage: Lin Liao, Dieter Fox, Henry Kautz, Learning and Inferring Transportation Routines, AAAI, 2004.	Marina Tum	Prof. Dr. Wolfram Burgard
4.	Echtzeit-Planung: Maxim Likhachev, Geoff Gordon, and Sebastian Thrun, ARA: Anytime A with Provable Bounds on Sub-Optimality, NIPS, 2003. Hintergrundmaterial: Maxim Likhachev, Geoff Gordon, and Sebastian Thrun, ARA: Formal Analysis Decision-Theoretic Planning: Structural Assumptions and Computational Leverage, (techn. report)*.	Mathias Maier	Maren Bennewitz
5.	Lokalisieren von dynamischen Objekten mit mehreren Robotern: Mathew Rosencrantz, Geoffrey Gordon, and Sebastian Thrun, Locating Moving Entities in Dynamic Indoor Environments with Teams of Mobile Robots, AAMAS, 2003.	Tonio Triebel	Maren Bennewitz
6.	Maschinelles Lernen: Nikunj Oza and Stuart Russell, Online Bagging and Boosting, In Eighth International Workshop on Artificial Intelligence and Statistics, 2001. Hintergrundmaterial: Yoav Freund and Robert E. Schapire, Experiments with a New Boosting Algorithm, International Conference on Machine Learning, 148-156, 1996.	Felix Steffenhagen	Alexander Kleiner
7.	Aktive Exploration mit autonomen Robotern: S. Thrun and K. Möller, Active exploration in dynamic environments, In Advances in Neural Information Processing Systems (NIPS), 1992. Hintergrundmaterial: S. Thrun, The role of exploration in learning control, In D.A. White and D.A. Sofge, editors, Handbook for Intelligent Control: Neural, Fuzzy and Adaptive Approaches, 1992.	Benjamin Lempp	Alexander Kleiner
8.	Agieren mit unpräziser Information: Fahiem Bacchus, Joseph Y. Halpern, and Hector J. Levesque, Reasoning about Noisy Sensors and Effectors in the Situation Calculus, Artificial Intelligence 111, 1999.	Axel Niese	Stefan Wölfl
9.	Dynamische Constraint-Satisfaction-Probleme: Pragnesh Jay Modi, Wei-Min Shen, Milind Tambe, and Makoto Yokoo, An Asynchronous Complete Method for Distributed Constraint Optimization, AAMAS, 2003. Hintergrundmaterial: Makoto Yokoo and Katsutoshi Hirayama, Algorithms for Distributed Constraint Satisfaction: A Review, Autonomous Agents and Multi-Agent Systems, Vol.3, No.2, pp.189--212, 2000.	Mario Philipps	Michael Brenner
10.	Kontinuierliche (Pfad-)Planung: S. Koenig, M. Likhachev and D. Furcy, Lifelong Planning A, Artificial Intelligence 155* , 2004.	Martin Guetlein	Michael Brenner
11.	Klassische Handlungsplanung: Patrik Haslum and Héctor Geffner, Admissible Heuristics for Optimal Planning, Fifth International Conference on Artificial Intelligence Planning and Scheduling, 2000.	Anne Schlicht	Malte Helmert
12.	Klassische Handlungsplanung: Fausto Giunchiglia and Paolo Traverso, Planning as Model Checking, Fifth European Conference on Planning, 1999.	Abgesagt	Abgesagt
13.	Multiagentenplanung mit Gegnermodellen: Patrick Riley and Manuela Veloso, Planning for Distributed Execution Through Use of Probabilistic Opponent Models, AIPS, 2002.	Klaus Rechert	Thilo Weigel
14.	ISO Lernen: B. Porr and F. Wörgötter, Isotropic Sequence Order Learning, Neural Computation, 2003. Hintergrundmaterial: Web page B. Porr and C. von Ferber and F. Wörgötter, ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm, Neural Computation, 2003.	Reimund Renner	Dr. Sven Behnke
15.	Lernen durch Imitation: C. Breazeal and D. Buchsbaum and J. Gray and D. Gatenby and B. Blumberg, Learning From And About Others: Towards Using Imitation to Bootstrap the Social Understanding of Others by Robots, to apper in Artificial Life , 2004. Hintergrundmaterial: C. Breazeal and B. Scassellati, Robots that imitate humans, Trends in Cognitive Science, 2002. R. P. Rao and A. N. Meltzoff, Imitation Learning in Infants and Robotics: Towards Probabilistic Computational Models, Proc. of AI and Simulation of Behaviour (ASIB), 2003.	Uli Siebold	Dr. Sven Behnke