Learning Strategies for Opponent Modeling in Poker

In poker, players tend to play sub-optimally due to theuncertainty in the game. Payoffs can be maximized byexploiting these sub-optimal tendencies. One way of realizingthis is to acquire the opponent strategy by recognizingthe key patterns in its style of play. Existing studieson opponent modeling in poker aim at predicting opponent’sfuture actions or estimating opponent’s hand.In this study, we propose a machine learning methodfor acquiring the opponent’s behavior for the purpose ofpredicting opponent’s future actions.We derived a numberof features to be used in modeling opponent’s strategy.Then, an ensemble learning method is proposed forgeneralizing the model. The proposed approach is testedon a set of test scenarios and shown to be effective.