Abstract
We present the agent programming language POGTGolog, which integrates explicit agent programming in Golog with game-theoretic multi-agent planning in partially observable stochastic games. It deals with the case of one team of cooperative agents under partial observability, where the agents may have different initial belief states and not necessarily the same rewards. POGTGolog allows for specifying a partial control program in a high-level logical language, which is then completed by an interpreter in an optimal way. To this end, we define a formal semantics of POGTGolog programs in terms of Nash equilibria, and we specify a POGTGolog interpreter that computes one of these Nash equilibria. We illustrate the usefulness of POGTGolog along a rugby scenario.
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References
Bacchus, F., Halpern, J.Y., Levesque, H.J.: Reasoning about noisy sensors and effectors in the situation calculus. Artif. Intell. 111, 171–208 (1999)
Boutilier, C., Reiter, R., Price, B.: Symbolic dynamic programming for first-order MDPs. In: Proceedings IJCAI-2001, pp. 690–700 (2001)
Boutilier, C., et al.: Decision-theoretic, high-level agent programming in the situation calculus. In: Proceedings AAAI-2000, pp. 355–362 (2000)
Ferrein, A., Fritz, C., Lakemeyer, G.: Using Golog for deliberation and team coordination in robotic soccer. Künstliche Intelligenz 1, 24–43 (2005)
Finzi, A., Lukasiewicz, T.: Game-theoretic agent programming in Golog. In: Proceedings ECAI-2004, pp. 23–27 (2004)
Finzi, A., Pirri, F.: Combining probabilities, failures and safety in robot control. In: Proceedings IJCAI-2001, pp. 1331–1336 (2001)
Goldman, C.V., Zilberstein, S.: Decentralized control of cooperative systems: Categorization and complexity analysis. J. Artif. Intell. Res. 22, 143–174 (2004)
Guestrin, C., et al.: Generalizing plans to new environments in relational MDPs. In: Proceedings IJCAI-2003, pp. 1003–1010 (2003)
Hansen, E.A., Bernstein, D.S., Zilberstein, S.: Dynamic programming for partially observable stochastic games. In: Proceedings AAAI-2004, pp. 709–715 (2004)
Pack Kaelbling, L., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1-2), 99–134 (1998)
Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proceedings ICML-1994, pp. 157–163 (1994)
McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of Artificial Intelligence. In: Machine Intelligence 4, pp. 463–502. Edinburgh University Press, Edinburgh (1969)
Nair, R., et al.: Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings. In: Proceedings IJCAI-2003, pp. 705–711 (2003)
Owen, G.: Game Theory, 2nd edn. Academic Press, London (1982)
Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Chichester (1994)
Reiter, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press, Cambridge (2001)
van der Wal, J.: Stochastic Dynamic Programming. Mathematical Centre Tracts, vol. 139. Morgan Kaufmann, San Francisco (1981)
von Neumann, J., Morgenstern, O.: The Theory of Games and Economic Behavior. Princeton University Press, Princeton (1947)
Yoon, S.W., Fern, A., Givan, B.: Inductive policy selection for first-order MDPs. In: Proceedings UAI-2002, pp. 569–576 (2002)
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Finzi, A., Lukasiewicz, T. (2007). Game-Theoretic Agent Programming in Golog Under Partial Observability. In: Freksa, C., Kohlhase, M., Schill, K. (eds) KI 2006: Advances in Artificial Intelligence. KI 2006. Lecture Notes in Computer Science(), vol 4314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69912-5_10
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DOI: https://doi.org/10.1007/978-3-540-69912-5_10
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