Strategy evaluation in extensive games with importance sampling
Proceedings of the 25th international conference on Machine learning
Artificial Intelligence
Successful performance via decision generalisation in no limit texas hold'em
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Decision generalisation from game logs in no limit texas Hold'em
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Poker games provide a useful testbed for modern Artificial Intelligence techniques. Unlike many classical game domains such as chess and checkers, poker includes elements of imperfect information, stochastic events, and one or more adversarial agents to interact with. Furthermore, in poker it is possible to win or lose by varying degrees. Therefore, it can be advantageous to adapt ones' strategy to exploit a weak opponent. A poker agent must address these challenges, acting in uncertain environments and exploiting other agents, in order to be highly successful. Arguably, poker games more closely resemble many real world problems than games with perfect information. In this brief paper, we outline Polaris, a Texas Hold'em poker program. Polaris recently defeated top human professionals at the Man vs. Machine Poker Championship and it is currently the reigning AAAI Computer Poker Competition winner in the limit equilibrium and no-limit events.