References [1] D. Billings, D. Papp, J. Schaeffer, D. Szafron ,Opponent modeling in poker

1. OPPONENT MODELLING IN POKER Souraj Misra Ayush Jain Mentor: Prof Amitabha Mukherjee Abstract Poker is a widely studied by game-theorists, is a challenging domain containing both Imperfect information and non-deterministic elements. The random shuffling cards provide a great Deal of uncertainty. The cards held by an opponent or in the remaining deck provide the imperfect information. Also players play deceptively to hide information. We intend to develop a method to make an intelligent decision in this imperfect environment with focus on the opponent modelling. Introduction Methods Methods Opponent Modelling •Weighting the Enumerations Different Weights Are used In place of equal probability for the hand evaluators. •Computing Initial Weights •Re-weighting Based on observed frequency of actions (raise , call ,fold). Imperfect Information Game Reasoning under uncertainty Unknown Opponent Cards and Different strategies of people. Betting Strategy Bet Prob=1/(1+exp(-a(d-f1))) Fold prob=1/(1+exp(a(d+f2)) Call prob=exp(-20(d+fc)^2) The Game of Poker(Texus Hold’Em) Further Improvements Results Approach We do a Single Opponent Modelling • First We do Pre-Flop Evaluation based upon income rates •Calculate Hand Strength And Hand Potential •Set Betting Strategy •Opponent Modelling Successful opponent modelling against Human is still a challenge .Above works when opponent strategy doesn’t Vary much with time No. of games required for above specified strategy to learn modelling is not of practical use as we don’t get same opponent to play that much no.of games .Bayesian network can be used to model uncertainity in games as well as opponent. References References [1] D. Billings, D. Papp, J. Schaeffer, D. Szafron ,Opponent modeling in poker Proc. AAAI-98, Madison, WI (1998), pp. 493–499 [2] D. Billings, A. Davidson, J. Schaeffer, D. Szafron ,The challenge of poker Artificial Intelligence, 134(1–2):201–240, 2002. [3] F. Southey, M. Bowling, B. Larson, C. Piccione, N. Burch, D. Billings, and C. Rayner. Bayes’ bluff: Opponent modelling in poker. In 21st Conference on Uncertainty in Artificial Intelligence, UAI’05) [4] D. Sklansky, M. Malmuth Hold'em Poker for Advanced Players (2nd Edition)Two Plus Two Publishing (1994) [5]Kevin B. Korb, Ann E.Nicholson and Nathalie Jitnah, Baysian Poker [6]Aaron Davidson, Opponent modelling in poker: Learing and acting in hostile environment. 2002 [7]Cactus Kev’s hand evaluation code Hand Strength And Hand Evaluation -Whaere Does Your Hand Stand as compared to all Others