Bayes-Relational Learning of Opponent Models from Incomplete Information in No-Limit Poker
نویسندگان
چکیده
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).
منابع مشابه
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تاریخ انتشار 2008