An Investigation of an Adaptive Poker Player

  • Graham Kendall
  • Mark Willdig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2256)

Abstract

Other work has shown that adaptive learning can be highly successful in developing programs which are able to play games at a level similar to human players and, in some cases, exceed the ability of a vast majority of human players. This study uses poker to investigate how adaptation can be used in games of imperfect information. An internal learning value is manipulated which allows a poker playing agent to develop its playing strategy over time. The results suggest that the agent is able to learn how to play poker, initially losing, before winning as the players strategy becomes more developed. The evolved player performs well against opponents with different playing styles. Some limitations of previous work are overcome, such as deal rotation to remove the bias introduced by one player always being the last to act. This work provides encouragement that this is an area worth exploring more fully in our future work.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barone L. and While L. 1998. Evolving Computer Opponents to Play a Game of Simplified Poker. In proceedings of the 1998 International Conference on Evolutionary Computation (ICEC’98), pp 108–113Google Scholar
  2. Barone L. and While L. 1999. An Adaptive Learning Model for Simplified Poker Using Evolutionary Algorithms. In proceedings of Congress of Evolutionary Computation 1999 (CEC’99), July 6–9, Washington DC, pp 153–160.Google Scholar
  3. Barone L. and While L. 2000. Adaptive Learning for Poker. In proceedings of the Genetic and Evolutionary Computation Conference 2000 (GECCO’2000), July 10–12, Las Vegas, Nevada, pp 560–573Google Scholar
  4. Billings, D., Papp, D., Schaeffer, J. and Szafron, D. 1998a. Poker as a Testbed for AI Research. In Proceedings of AI’98, The Twelfth Canadian Conference on Artificial Intelligence, Mercer, R.E., and Neufeld, E. (eds), Advances in Artificial Intelligence, Springer-Verlag, pp 228–238Google Scholar
  5. Billings, D., Papp, D., Schaeffer, J. and Szafron, D. 1998b. Opponent Modelling in Poker. In Proceedings of the 15th National AAAI Conference (AAAI-98), pp 493–499Google Scholar
  6. Billings, D., Pea, L, P., Schaeffer, J. and Szafron, D. 1999. Using Probabilistic Knowledge and Simulation to Play Poker. In Proceedings of AAAI-99 (Sixteenth National Conference of the Association for Artificial Intelligence).Google Scholar
  7. Chellapilla, K. and Fogel, D. B. 2000. Anaconda Defeats Hoyle 6-0: A Case Study Competing an Evolved Checkers Program against Commercially Available Software. In Proceedings of Congress on Evolutionary Computation, July 16–19 2000, La Jolla Marriot Hotel, La Jolla, California, USA, pp 857–863Google Scholar
  8. Findler N. 1977. Studies in machine cognition using the game of poker. Communications of the ACM, 20(4):230–245.Google Scholar
  9. Fogel D. 2001. Blondie24: Playing at the Edge of AI, Morgan Kaufmann, ISBN 1-55860-783-8Google Scholar
  10. Hamilton, S., Garber, L. 1997. Deep Blue’s Hardware-Software Synergy. IEEE, pp 29–35Google Scholar
  11. Kendall, G and Whitwell, G. 2001. An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics. In proceedings of Congress of Evolutionary Computation 2001 (CEC 2001), May 27–30 2001, COEX, Seoul, Korea, pp 995–1002.Google Scholar
  12. Koller, D. and Pfeffer, A. 1997. Representations and Solutions for Game-Theoritic Problems. Artificial Intelligence 94(1–2), pp 167–215Google Scholar
  13. Neumann, J von and Morgenstern, O. 1944. Theory of Gamesand Economic Behavior. Princeton, N.J.: Princeton University PressGoogle Scholar
  14. Samuel A. L. 1959. Some Studies in Machine Learning using the Game of Checkers. IBM Journal of Research and Development, 3(3), pp 210–229Google Scholar
  15. Samuel A. L. 2000. Some Studies in Machine Learning using the Game of Checkers. IBM Journal of Research and Development, Vol. 44 No. 1/2 January/March, pp 207–226Google Scholar
  16. Schaeffer, J. 1996. One Jump Ahead: Challenging Human Supremacy in Checkers, Springer, BerlinGoogle Scholar
  17. Schaeffer, J., Billings, D., Pea, L, P. and Szafron, D. 1999. Learning to Play Strong Poker. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99) (invited paper)Google Scholar
  18. Sklansky, D. 1994. Theory of Poker. Two Plus Two Publishing, ISBN 1-880685-00-0Google Scholar
  19. Sklansky, D. 1996. Hold’em Poker. Two Plus Two Publishing, ISBN 1-880685-08-6Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Graham Kendall
    • 1
  • Mark Willdig
    • 1
  1. 1.School of Computer Science & ITThe University of NottinghamNottinghamUK

Personalised recommendations