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Can opponent models aid poker player evolution?
2008
2008 IEEE Symposium On Computational Intelligence and Games
We investigate the impact of Bayesian opponent modeling upon the evolution of a player for a simplified poker game. Through the evolution of Artificial Neural Networks using NEAT we create and compare players both utilizing and ignoring Bayesian opponent beliefs. We test the effectiveness of this model against various collections of dynamic and partially randomized opponents and find that using a Bayesian opponent model enhances our AI players even when dealing with a previously unseen
doi:10.1109/cig.2008.5035617
dblp:conf/cig/BakerCRJ08
fatcat:nxexkyavpzgi7dnz2levheiktu