A Texas Hold’em Poker Player Based on Automated Abstraction and Real-time Equilibrium Computation
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.