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

We propose an opponent modeling approach for NoLimit 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).