Games have generally well-defined rules and goals and therefore representing an ideal environment for examining complex topics in machine intelligence. Previous research has mainly focused on deterministic zero-sum games providing perfect information such as checkers, othello, and foremost chess. These types of games have a crucial thing in common, every player holds anytime complete knowledge of the entire game state.
The game of Poker represents a challenging domain, since it contains imperfect information, deception, and non-deterministic elements. An extensive degree of uncertainty is caused by the random shuffling of the card deck. Private cards held by opponents and concealed cards remaining in the deck represent the imperfect information elements of the game. Opponents are provoked to play deceptively for the purpose of hiding their pocket cards. These characteristics prevent traditional game-tree search methods from working properly.
We present a new approach for dynamically model the opponent's behaviour in Texas Hold'em poker. The model highlights a way to partially solve the problem of imperfect information elements. This objective is primarily achieved by applying artificial neural networks.