Automating Texas Hold’em Poker with PLICAS

  • Michael Schwind
  • Christian Friedrich
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 102)

Abstract

Influenced by the possibilities of the Internet poker has become a popular online game. Spurred by this development, automated poker got into the focus of research in game theory (GT), artificial intelligence (AI) and multi-agent systems (MAS). This paper describes the development and evaluation of PLICAS, a poker bot designed for the ‘Texas Hold’em Fixed Limit Heads-up’ variant. The poker bot integrates approaches, such as opponent modeling, abstraction techniques, and case-based reasoning. PLICAS also introduces simulation-based methods for the exploitation of the opponent’s play. Participation in the 2010 AAAI Computer Poker Competition (ACPC) shows that PLICAS has a lot of potential but suffers from a vulnerable opponent modeling strategy.

Keywords

Multi-agent systems Artificial intelligence Game theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Billings, D., Burch, N., Davidson, A., Holte, R., Schauenberg, T., Schaeffer, J., Szafron, D.: Approximating game-theoretic optimal strategies for full-scale poker. In: Proc. of the Int. Joint Conf. on Artificial Intelligence (ICAI 2003), Las Vegas, NV, pp. 661–668 (2003)Google Scholar
  2. 2.
    Billings, D., Papp, D., Schaeffer, J., Szafron, D.: Opponent modeling in poker. In: Proc. of the Fifteenth Nat. Conf. on Artificial Intelligence (AAAI 1998), Madison, WI, pp. 493–499. AAAI Press (1998)Google Scholar
  3. 3.
    Billings, D., Pena, L., Schaeffer, J., Szafron, D.: Using probabilistic knowledge and simulation to play poker. In: Proc. of the Sixteenth Nat. Conf. on Artificial Intelligence (AAAI 1999), Orlando, FL, pp. 697–703 (1999)Google Scholar
  4. 4.
    Davidson, A.: Using artificial neural networks to model opponents in texas hold’em. Res. Proj. Review CMPUT 499, Poker Res. Group, Univ. of Alberta, CA (1999)Google Scholar
  5. 5.
    Davidson, A., Billings, D., Schaeffer, J., Szafron, D.: Improved opponent modeling in poker. In: Proceedings of the Int. Conf. on Artificial Intelligence (ICAI 2000), Las Vegas, Nevada, pp. 493–499 (2000)Google Scholar
  6. 6.
    Gilpin, A., Sorensen, T.B., Sandholm, T.: Potential-aware automated abstraction of sequential games, and holistic equilibrium analysis of texas hold’em poker. In: Proc. of the Nat. Conf. on Artificial Intelligence (AAAI 2007), Vancouver, CA (2007)Google Scholar
  7. 7.
    Hamilton, S., Garber, L.: Deep blue’s hardware-software synergy. Computer 30, 29–35 (1997)CrossRefGoogle Scholar
  8. 8.
    Johanson, M.: Robust strategies and counter-strategies: Building a champion level computer poker player. Master’s thesis, University of Alberta (2007)Google Scholar
  9. 9.
    Koller, D., Pfeffer, A.: Representations and solutions for game-theoretic problems. Artificial Intelligence 94, 167–215 (1997)CrossRefGoogle Scholar
  10. 10.
    Kuhn, H.W.: Simplified two-person poker. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games, vol. 1, pp. 97–103. Princeton University Press (1950)Google Scholar
  11. 11.
    Lockett, A., Miikkulainen, R.: Evolving opponent models for texas hold’em. In: Proc. of the 2008 IEEE Conf. on Computational Intelligence in Games, Perth. IEEE (2008)Google Scholar
  12. 12.
    Nash, J.F., Shapley, L.S.: A simple 3-person poker game. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games, vol. 1, pp. 105–116. Princeton University Press (1950)Google Scholar
  13. 13.
    Neumann, J.V., Morgenstern, O.: Theory of Games and Economic Behavior. John Wiley (1944)Google Scholar
  14. 14.
    Rubin, J., Watson, I.: A Memory-Based Approach to Two-Player Texas Hold’em. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 465–474. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Rubin, J., Watson, I.: Similarity-Based Retrieval and Solution Re-use Policies in the Game of Texas Hold’em. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 465–479. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Schaeffer, J.: One jump ahead: challenging human supremacy in checkers. Springer, NY (1997)Google Scholar
  17. 17.
    Schauenberg, T.: Opponent modeling and search in poker. Master’s thesis, University of Alberta, Department of Computing Science, Edmonton, Alberta (2006)Google Scholar
  18. 18.
    Sklansky, D.: Hold’em Poker: A Complete Guide to Playing the Game. Two Plus Two Publishing, Henderson, NV, USA (1997)Google Scholar
  19. 19.
    Southey, F., Bowling, M., Larson, B., Piccione, C., Burch, N., Billings, D., Rayner, C.: Bayes’ bluff: Opponent modeling in poker. In: Proc. of the Twenty-First Conf. on Uncertainty in Artificial Intelligence (UAI), pp. 550–558 (2005)Google Scholar
  20. 20.
    Watson, I., Rubin, J.: CASPER: A Case-Based Poker-Bot. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 594–600. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Zinkevich, M., Johanson, M., Bowling, M., Piccione, C.: Regret minimization in games with incomplete information. In: Advances in Neural Information Processing Systems 20 (NIPS), pp. 1729–1736 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michael Schwind
    • 1
  • Christian Friedrich
    • 2
  1. 1.IT-based Logistics, Institute for Information SystemsGoethe University FrankfurtFrankfurt MainGermany
  2. 2.Business Information Systems and Operations ResearchTechnical University KaiserslauternKaiserslauternGermany

Personalised recommendations