Game-Theoretic Agent Programming in Golog Under Partial Observability

  • Alberto Finzi
  • Thomas Lukasiewicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4314)

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

  1. 1.
    Bacchus, F., Halpern, J.Y., Levesque, H.J.: Reasoning about noisy sensors and effectors in the situation calculus. Artif. Intell. 111, 171–208 (1999)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Boutilier, C., Reiter, R., Price, B.: Symbolic dynamic programming for first-order MDPs. In: Proceedings IJCAI-2001, pp. 690–700 (2001)Google Scholar
  3. 3.
    Boutilier, C., et al.: Decision-theoretic, high-level agent programming in the situation calculus. In: Proceedings AAAI-2000, pp. 355–362 (2000)Google Scholar
  4. 4.
    Ferrein, A., Fritz, C., Lakemeyer, G.: Using Golog for deliberation and team coordination in robotic soccer. Künstliche Intelligenz 1, 24–43 (2005)Google Scholar
  5. 5.
    Finzi, A., Lukasiewicz, T.: Game-theoretic agent programming in Golog. In: Proceedings ECAI-2004, pp. 23–27 (2004)Google Scholar
  6. 6.
    Finzi, A., Pirri, F.: Combining probabilities, failures and safety in robot control. In: Proceedings IJCAI-2001, pp. 1331–1336 (2001)Google Scholar
  7. 7.
    Goldman, C.V., Zilberstein, S.: Decentralized control of cooperative systems: Categorization and complexity analysis. J. Artif. Intell. Res. 22, 143–174 (2004)MATHMathSciNetGoogle Scholar
  8. 8.
    Guestrin, C., et al.: Generalizing plans to new environments in relational MDPs. In: Proceedings IJCAI-2003, pp. 1003–1010 (2003)Google Scholar
  9. 9.
    Hansen, E.A., Bernstein, D.S., Zilberstein, S.: Dynamic programming for partially observable stochastic games. In: Proceedings AAAI-2004, pp. 709–715 (2004)Google Scholar
  10. 10.
    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)MATHCrossRefGoogle Scholar
  11. 11.
    Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proceedings ICML-1994, pp. 157–163 (1994)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Nair, R., et al.: Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings. In: Proceedings IJCAI-2003, pp. 705–711 (2003)Google Scholar
  14. 14.
    Owen, G.: Game Theory, 2nd edn. Academic Press, London (1982)MATHGoogle Scholar
  15. 15.
    Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Chichester (1994)MATHGoogle Scholar
  16. 16.
    Reiter, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press, Cambridge (2001)MATHGoogle Scholar
  17. 17.
    van der Wal, J.: Stochastic Dynamic Programming. Mathematical Centre Tracts, vol. 139. Morgan Kaufmann, San Francisco (1981)MATHGoogle Scholar
  18. 18.
    von Neumann, J., Morgenstern, O.: The Theory of Games and Economic Behavior. Princeton University Press, Princeton (1947)Google Scholar
  19. 19.
    Yoon, S.W., Fern, A., Givan, B.: Inductive policy selection for first-order MDPs. In: Proceedings UAI-2002, pp. 569–576 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alberto Finzi
    • 1
    • 2
  • Thomas Lukasiewicz
    • 2
    • 1
  1. 1.Institut für Informationssysteme, Technische Universität Wien, Favoritenstraße 9-11, A-1040 ViennaAustria
  2. 2.Dipartimento di Informatica e Sistemistica, Università di Roma “La Sapienza”, Via Salaria 113, I-00198 RomeItaly

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