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