Abstract
Building a high-performance poker-playing program is a challenging project. The best program to date, PsOpti, uses game theory to solve a simplified version of the game. Although the program plays reasonably well, it is oblivious to the opponent’s weaknesses and biases. Modeling the opponent to exploit predictability is critical to success at poker. This paper introduces Vexbot, a program that uses a game-tree search algorithm to compute the expected value of each betting option, and does real-time opponent modeling to improve its evaluation function estimates. The result is a program that defeats PsOpti convincingly, and poses a much tougher challenge for strong human players.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Horvitz, E., Breese, L., Heckerman, D., Hovel, D., Rommeke, K.: The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In: UAI, pp. 256–265 (1998)
Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.): UM 2003. LNCS, vol. 2702. Springer, Heidelberg (2003)
Weld, D., Anderson, C., Domingos, P., Etzioni, O., Lau, T., Gajos, K., Wolfman, S.: Automatically personalizing user interfaces. In: IJCAI, pp. 1613–1619 (2003)
Billings, D., Burch, N., Davidson, A., Holte, R., Schaeffer, J., Schauenberg, T., Szafron, D.: Approximating game-theoretic optimal strategies for full-scale poker. In: IJCAI, pp. 661–668 (2003)
Jansen, P.: Using Knowledge about the Opponent in Game-Tree Search. PhD thesis, Computer Science, Carnegie-Mellon University (1992)
Carmel, D., Markovitch, S.: Opponent modeling in adversary search. In: AAAI, pp. 120–125 (1996)
Iida, H., Uiterwijk, J.W.H.M., van den Herik, H.J., Herschberg, I.S.: Potential applications of opponent-model search. ICCA Journal 16, 201–208 (1993)
Billings, D., Davidson, A., Schaeffer, J., Szafron, D.: The challenge of poker. Artificial Intelligence 134, 201–240 (2002)
Dahl, F.: A reinforcement learning algorithm to simplified two-player Texas Hold’em poker. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 85–96. Springer, Heidelberg (2001)
Korb, K., Nicholson, A., Jitnah, N.: Bayesian poker. In: UAI, pp. 343–350 (1999)
Findler, N.: Studies in machine cognition using the game of poker. CACM 20, 230–245 (1977)
von Neumann, J., Morgenstern, O.: The Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)
Kuhn, H.W.: A simplified two-person poker. Contributions to the Theory of Games 1, 97–103 (1950)
Billings, D., Papp, D., Schaeffer, J., Szafron, D.: Opponent modeling in poker. In: AAAI, pp. 493–499 (1998)
Billings, D., Peña, L., Schaeffer, J., Szafron, D.: Using probabilistic knowledge and simulation to play poker. In: AAAI, pp. 697–703 (1999)
Koller, D., Pfeffer, A.: Representations and solutions for game-theoretic problems. Artificial Intelligence, 167–215 (1997)
Buro, M.: Solving the oshi-zumo game. Advances in Computer Games 10, 361–366 (2004)
Billings, D.: The first international RoShamBo programming competition. International Computer Games Association Journal 23(3-8), 42–50 (2000)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (2003)
Billings, D.: Vexbot wins poker tournament. International Computer Games Association Journal 26, 281 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Billings, D. et al. (2006). Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games. In: van den Herik, H.J., Björnsson, Y., Netanyahu, N.S. (eds) Computers and Games. CG 2004. Lecture Notes in Computer Science, vol 3846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11674399_2
Download citation
DOI: https://doi.org/10.1007/11674399_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32488-1
Online ISBN: 978-3-540-32489-8
eBook Packages: Computer ScienceComputer Science (R0)