Simulation of a Texas Hold'Em poker player

Imperfect information environments are amongst common research subjects in the field of Artificial Intelligence. A game of poker is a good example of such an environment. As the popularity of the game grew, so did the interest in implementing a functioning automatized poker player. Approaches to this problem include various Machine Learning techniques like Bayesian decision networks, various Case-based reasoning (CBR) techniques and reinforcement learning. For a player to play well it is not enough to know just the probability estimates of one's own hand. A player must adjust his strategy according to his estimate of the opponents' strategies and an estimate of opponents' hand strength. This paper explores the usage of the k - Nearest Neighbors technique, an example of CBR techniques, in implementing an automatized poker player. As a result, an average player able to cope with most in-game situations was developed. The main difference from a model based on optimal mathematical play is that the developed player seems more human, which makes its actions harder to predict. Numerous simulations on the developed testing model show that a small but stable profit is gained by the implemented automatized player.