Bayesian Opponent Modeling in a Simple Poker Environment

Abstract

In this paper, we use a simple poker game to investigate Bayesian opponent modeling. Opponents are defined in four distinctive styles, and tactics are developed which defeat each of the respective styles. By analyzing the past actions of each opponent, and comparing to action related probabilities, the most challenging opponent is identified, and the strategy employed is one that aims to counter that player. The opponent modeling player plays well against non-reactive player styles, and also performs well when compared to a player that knows the exact styles of each opponent in advance