An Exploitative Monte-Carlo Poker Agent

  • Immanuel Schweizer
  • Kamill Panitzek
  • Sang-Hyeun Park
  • Johannes Fürnkranz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5803)

Abstract

We describe the poker agent AKI-RealBot which participated in the 6-player Limit Competition of the third Annual AAAI Computer Poker Challenge in 2008. It finished in second place, its performance being mostly due to its superior ability to exploit weaker bots. This paper describes the architecture of the program and the Monte-Carlo decision tree-based decision engine that was used to make the bot’s decision. It will focus the attention on the modifications which made the bot successful in exploiting weaker bots.

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References

  1. 1.
    Billings, D.: Thoughts on RoShamBo. ICGA Journal 23, 3–8 (2000)Google Scholar
  2. 2.
    Billings, D., Castillo, L.P., Schaeffer, J., Szafron, D.: Using probabilistic knowledge and simulation to play poker. In: AAAI/IAAI, pp. 697–703 (1999)Google Scholar
  3. 3.
    Billings, D., Davidson, A., Schaeffer, J., Szafron, D.: The challenge of poker. Artif. Intell. 134(1-2), 201–240 (2002)CrossRefMATHGoogle Scholar
  4. 4.
    Bouzy, B.: Associating domain-dependent knowledge and Monte Carlo approaches within a Go program. In: Joint Conference on Information Sciences, pp. 505–508 (2003)Google Scholar
  5. 5.
    Ginsberg, M.L.: Gib: Steps toward an expert-level Bridge-playing program. In: Proc. of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 584–589 (1999)Google Scholar
  6. 6.
    Metropolis, N., Ulam, S.: The Monte Carlo method. J. Amer. Stat. Assoc. 44, 335–341 (1949)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Schweizer, I., Panitzek, K., Park, S.-H., Fürnkranz, J.: An exploitative Monte-Carlo poker agent. Technical Report TUD-KE-2009-02, TU Darmstadt, Knowledge Engineering Group (2009), http://www.ke.informatik.tu-darmstadt.de/publications/reports/tud-ke-2009-02.pdf
  8. 8.
    Tesauro, G.: Temporal difference learning and TD-Gammon. Commun. ACM 38(3), 58–68 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Immanuel Schweizer
    • 1
  • Kamill Panitzek
    • 1
  • Sang-Hyeun Park
    • 1
  • Johannes Fürnkranz
    • 1
  1. 1.Knowledge Engineering GroupTU DarmstadtDarmstadtGermany

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