Bayes-relational learning of opponent models from incomplete information in no-limit poker
Marc Ponsen, Jan Ramon, Tom Croonenborghs, Kurt Driessens, Karl Tuyls · TU/e Research Portal · 2008
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).