Bayes-Relational Learning of Opponent Models from Incomplete Information in No-Limit Poker

Marc Ponsen, Jan Ramon,Tom Croonenborghs,Kurt Driessens,Karl Tuyls

We propose an opponent modeling approach for No-Limit Texas Hold'em poker that starts from a (learned) prior, i.e., general expectations about opponent behavior and learns a relational regression tree-function that adapts these priors to specific opponents. An important asset is that this approach can learn from incomplete information (i.e. without knowing all players' hands in training games).

Subjects: 12. Machine Learning and Discovery; 7.1 Multi-Agent Systems

Submitted: Apr 15, 2008

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