Evolving Nash-optimal poker strategies using evolutionary computation

Frontiers of Computer Science in China

Volumn 3, Issue 1, 2009, Pages 73-91

  Quek, Hanyang ^a   Woo, Chunghoong ^a   Tan, Kaychen ^a   Tay, Arthur ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Evolutionary algorithm; Game theory; Nash equilibrium; Poker

Indexed keywords

ARTIFICIAL INTELLIGENCE; GAME THEORY; KNOWLEDGE REPRESENTATION; OPTIMIZATION; TELECOMMUNICATION NETWORKS;

COMPLEX INFORMATIONS; EVOLUTIONARY COMPUTATIONS; EXPERT KNOWLEDGE; GAME ENGINES; INTRANSITIVITY; NASH EQUILIBRIUM; POKER; SIMULATED RESULTS; TEXAS HOLD'EM;

EVOLUTIONARY ALGORITHMS;

EID: 62349130798     PISSN: 16737350     EISSN: 16737466     Source Type: Journal    
DOI: 10.1007/s11704-009-0007-5     Document Type: Article

References (19)

1. Using artificial neural networks to model opponents in Texas Hold'em. Machine Learning in Games Undergraduate Research Course, CMPUT 499, Computer Poker Research Group, University of Alberta, 1999
2. Korb K B, Nicholson A E, Jitnah N. Bayesian poker. In: Proceedings of the Uncertainty in Artificial Intelligence (UAI), 1999, 343-350
3. Billings D. Computer Poker. M. Sc. research essay, Computer Poker Research Group. Alberta: University of Alberta, 1995
4. Cosmic Log, MSNBC. Human Beat Poker Bot • • • Barely, 2007, http://cosmiclog.msnbc.msn.com/archive/2007/07/25/289607.aspx
5. Holland J H. Genetic Algorithms, 2005, http://www.econ.iastate.edu/ tesfatsi/holland.GAIntro.htm
6. J. Nash 1950 Equilibrium points in n-person games Proceedings of the National Academy of Sciences of the United States of America 36 1 48 49
7. D. Billings A. Davidson J. Schaeffer D. Szafron 2002 The challenge of poker Artificial Intelligence Journal 134 1-2 201 240
8. Billings D, Papp D, Schaeffer J, Szafron D. Opponent modeling in poker. In: Proceedings of the fifteen AAAI conference, 1998, 493-499
9. Billings D, Papp D, Schaeffer J, Szafron D. Poker as a testbed for machine intelligence research. In: Mercer R, Neufeld E, eds. Advances in Artificial Intelligence. Springer-Verlag, 1998, 1-15
10. Papp D. Dealing with imperfect information in poker. M.Sc. thesis. Alberta: University of Alberta, 1998
11. Billings D, Pena L, Schaeffer J, Szafron D. Using probabilistic knowledge and simulation to play poker. In: Proceedings of the sixteenth AAAI conference, 1999, 697-703
12. Davidson A, Billings D, Schaeffer J, Szafron D. Improved opponent modeling in poker. In: Proceedings of the 2000 International Conference on Artificial Intelligence (ICAI'2000), 2000, 1467-1473
13. Express News, University of Alberta. U of A researchers win computer poker title, 2006, http://www.expressnews.ualberta.ca/article.cfm?id=7789
14. Billings D, Burch N, Davidson A, Holte R, Schaeffer J, Scauenberg T, Szafron D. Approximating game-theoretic optimal strategies for full-scale Poker. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), 2003, 661-668
15. L. Barone L. While 1999 An adaptive learning model for simplified Poker using evolutionary algorithms Proceedings of the Congress on Evolutionary Computation (GECCO-1999) 1 153 160
16. Barone L, While L. Adaptive learning for poker. In: Proceedings of the Genetic and Evolutionary Computation Conference 2000 (GECCO-2000), 2000, 566-573
17. Oliehoek F, Vlassis N, De Jong E. Coevolutionary Nash in poker games. In: Proceedings of the seventeenth Belgian-Dutch Conference on Artificial Intelligence (BNAIC-2005), 2005, 188-193
18. The Early Show, CBS News. Poker's Popularity Surging, 2004, http://www.cbsnews.com/stories/2004/12/25/earlyshow/living/main663053.shtml
19. H. W. Kuhn 1953 Extensive games and the problem of information Proceedings of Contributions to the Theory of Games, Annals of Mathematics Studies 2 28 193 216