Poker as a testbed for AI research

  • Darse Billings
  • Denis Papp
  • Jonathan Schaeffer
  • Duane Szafron
Posters
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1418)

Abstract

For years, games researchers have used chess, checkers and other board games as a testbed for artificial intelligence research. The success of world-championship-caliber programs for these games has resulted in a number of interesting games being overlooked. Specifically, we show that poker can serve as an interesting testbed for machine intelligence research related to decision making problems. Poker is a game of imperfect knowledge, where multiple competing agents must deal with risk management, agent modeling, unreliable information and deception, much like decision-making applications in the real world. The heuristic search and evaluation methods successfully employed in chess are not helpful here. This paper outlines the difficulty of playing strong poker, and describes our first steps towards building a world-class poker-playing program.

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References

  1. 1.
    J. von Neumann and O. Morgenstern, Theory of Games and Economic Behavior, Princeton University Press, 1944.Google Scholar
  2. 2.
    N. Ankeny, Poker Strategy: Winning with Game Theory, Basic Books, Inc., 1981.Google Scholar
  3. 3.
    M. Sakaguchi and S. Sakai, Solutions of Some Three-person Stud and Draw Poker, Mathematics Japonica 37, 6 (1992), 1147–1160.Google Scholar
  4. 4.
    D. Sklansky and M. Malmuth, Hold'em Poker for Advanced Players, Two Plus Two Publishing, 1994.Google Scholar
  5. 5.
    N. Findler, Studies in Machine Cognition Using the Game of Poker, Communications of the ACM 20, 4 (1977), 230–245.CrossRefGoogle Scholar
  6. 6.
    D. Billings, Computer Poker, M.Sc. thesis, Dept. of Computing Science, University of Alberta, 1995.Google Scholar
  7. 7.
    D. Koller and A. Pfeffer, Representations and Solutions for Game-Theoretic Problems, Artificial Intelligence 94, 1–2 (1997), 167–215.CrossRefGoogle Scholar
  8. 8.
    D. Carmel and S. Markovitch, Incorporating Opponent Models into Adversary Search, AAAI, 19969, 120–125.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Darse Billings
    • 1
  • Denis Papp
    • 1
  • Jonathan Schaeffer
    • 1
  • Duane Szafron
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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