Monte-carlo tree search in poker using expected reward distributions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5828 LNAI, Issue , 2009, Pages 367-381

  Van Den Broeck, Guy ^a   Driessens, Kurt ^a   Ramon, Jan ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

EXPECTED VALUES; GAME TREES; HIDDEN INFORMATION; MONTE CARLO; NEW STRATEGY; OPPONENT MODELS; TEXAS HOLD'EM; TREE SEARCH;

COMPUTER CRIME; MONTE CARLO METHODS; ROBOT LEARNING;

PROBABILITY DISTRIBUTIONS;

EID: 70549113669     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-05224-8_28     Document Type: Conference Paper

References (12)

1. Gilpin, A., Sandholm, T., Sørensen, T.: A heads-up no-limit Texas Hold'em poker player: discretized betting models and automatically generated equilibrium-finding programs. In: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, vol. 2, pp. 911-918 (2008)
2. Billings, D.: Algorithms and assessment in computer poker. PhD thesis, Edmonton, Alta, Canada (2006)
3. Kocsis, L., Szepesvari, C.: Bandit based monte-carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282-293. Springer, Heidelberg (2006)
4. Russell, S., Norvig, P.: Artificial intelligence: A modern approach. Prentice Hall, Englewood Cliffs (2003)
5. Suffecool, K.: Cactus kev's poker hand evaluator (July 2007), http://www.suffecool.net/poker/evaluator.html
6. Witten Ian, H., Eibe, F.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)
7. Wang, Y., Witten, I.: Induction of model trees for predicting continuous classes (1996)
8. Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M(J.) (eds.) CG 2006. LNCS, vol. 4630, pp. 72-83. Springer, Heidelberg (2007)
9. Gelly, S., Wang, Y.: Exploration exploitation in go: UCT for Monte-Carlo go. In: Twentieth Annual Conference on Neural Information Processing Systems, NIPS 2006 (2006)
10. Chaslot, G., Winands, M., Herik, H., Uiterwijk, J., Bouzy, B.: Progressive strategies for monte-carlo tree search. New Mathematics and Natural Computation 4(3), 343 (2008)
11. Van Lishout, F., Chaslot, G., Uiterwijk, J.: Monte-Carlo Tree Search in Backgammon. In: Computer Games Workshop, pp. 175-184 (2007)
12. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2-3), 235-256 (2002)