Ph.D., University of Alberta, Edmonton, Alberta (2006) Two Plus Two (2002)īilligs, D.: Algorithms and Assessment in Computer Poker. Sklansky, D.: The Theory of Poker: A Professional Poker Player Teaches You How to Think Like One.

M.Sc., University of Alberta, Edmonton, Alberta, Canada (2002) American Association for Artificial Intelligence, Madison (1998)ĭavidson, A.: Opponent modeling in poker. Princeton University Press, Princeton (2002)īillings, D., Papp, D., Schaeffer, J., Szafron, D.: Opponent modeling in poker. Hsu, F.-H.: Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. To solve this problem, we created an agent that uses a strategy that combines several tactics from different players.By using the combined strategy, the agentgreatly improved its performance against adversaries capable of modeling opponents. However, this approach alone is insufficient to create a competitive agent, as generated strategies are static, meaning that they can’t adapt to different situations. The agent approximately follows the tactics from the human players, thus validating this model. To validate and test the defined player model, an agent that follows the learned tactic was created.

To accomplish this goal, we defined a classification problem that associates a given game state with the action that was performed by the player. In this paper we propose the creation of an artificial Poker player through the analysis of past games between human players, with money involved. The development of competitive artificial Poker players is a challenge toArtificial Intelligence (AI) because the agent must deal with unreliable information and deception which make it essential to model the opponents to achieve good results.

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