A Texas Hold'em poker player based on automated abstraction and real-time equilibrium computation

Authors:
Andrew Gilpin;Tuomas Sandholm
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Year:
2006

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Abstract

We demonstrate our game theory-based Texas Hold'em poker player. To overcome the computational difficulties stemming from Texas Hold'em's gigantic game tree, our player uses automated abstraction and real-time equilibrium approximation. Our player solves the first two rounds of the game in a large off-line computation, and solves the last two rounds in a real-time equilibrium approximation. Participants in the demonstration will be able to compete against our opponent and experience first-hand the cognitive abilities of our player. Some of the techniques used by our player, which does not directly incorporate any poker-specific expert knowledge, include such poker techniques as bluffing, slow-playing, check-raising, and semi-bluffing, all techniques normally associated with human play.