AAAI-17 Tutorial on Computer Poker

Poker has been studied academically since the founding of game theory and in fact may have even been the inspiration for the field: the only motivating problem described in John Nash's PhD thesis, which defined and proved existence of the central solution concept, was actually a three-player poker game. Such interest has not waned over time. Last year when a computer program developed at Carnegie Mellon University competed against the strongest human two-player no-limit Texas hold 'em players in the inaugural Brains vs. Artificial Intelligence Competition, thousands of poker fans followed with excitement. Earlier that year another monumental breakthrough was attained, as the two-player limit variation of Texas hold 'em (the smallest variant played competitively by humans) was "essentially weakly solved'' (i.e., an epsilon-Nash equilibrium was computed for such a small epsilon to be statistically indistinguishable from zero in a human lifetime) by researchers at the University of Alberta. This result was published in the journal Science. Poker, and particularly Texas hold 'em, is tremendously popular for humans, and online poker is a multi-billion dollar industry. Computer poker has proved to be one of the most visible applications of research in computational game theory.


The tutorial will take place 9:00AM-1:00PM on Saturday February 4. Here is the schedule, and here are slides for part 1, part 2, and part 3.


Sam Ganzfried is an assistant professor in computer science at Florida International University. He received a PhD in computer science from Carnegie Mellon in 2015. He created two-player no-limit Texas hold 'em agent Claudico that competed in the Brains vs. Artificial Intelligence Competition against the strongest humans in the world. []

Johannes Heinrich is completing his PhD in Computer Science at University College London, researching "Reinforcement Learning from Self-Play in Imperfect-Information Games." His PhD was supported by DeepMind, where he worked on game-theoretic approaches to reinforcement learning. He developed SmooCT, an MCTS-based agent that won 3 silver medals at the ACPC. []

Kevin Waugh is a research scientist at Facebook. He has been involved with the computer poker competition since 2008, competing while obtaining his MSc at University of Alberta under Prof Michael Bowling. Subsequently, he studied under Prof Drew Bagnell at Carnegie Mellon University. He has chaired the competition since 2014. []