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Bayes-relational learning of opponent models from incomplete information in no-limit poker (2008)
| Content Provider | CiteSeerX |
|---|---|
| Author | Driessens, Kurt Tuyls, Karl Croonenborghs, Tom Ponsen, Marc Ramon, Jan |
| Description | In Twenty-third Conference of the Association for the Advancement of Artificial Intelligence (AAAI-08 |
| Abstract | 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 behav-ior 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 in-formation (i.e. without knowing all players ’ hands in training games). |
| File Format | |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | General Expectation Opponent Model No-limit Texas Hold Em Poker No-limit Poker Incomplete In-formation Relational Regression Tree-function Player Hand Important Asset Specific Opponent Bayes-relational Learning |
| Content Type | Text |
| Resource Type | Conference Proceedings |
