Bayesian Poker
نویسندگان
چکیده
Poker is ideal for testing automated reason ing under uncertainty. It introduces un certainty both by physical randomization and by incomplete information about op ponents' hands. Another source of uncer tainty is the limited information available to construct psychological models of opponents, their tendencies to bluff, play conservatively, reveal weakness, etc. and the relation be tween their hand strengths and betting be haviour. All of these uncertainties must be assessed accurately and combined effectively for any reasonable level of skill in the game to be achieved, since good decision making is highly sensitive to those tasks. We de scribe our Bayesian Poker Program (BPP) , which uses a Bayesian network to model the program's poker hand, the opponent's hand and the opponent's playing behaviour con ditioned upon the hand, and betting curves which govern play given a probability of win ning. The history of play with opponents is used to improve BPP's understanding of their behaviour. We compare BPP experimentally with: a simple rule-based system; a program which depends exclusively on hand probabil ities (i.e., without opponent modeling); and with human players. BPP has shown itself to be an effective player against all these opponents, barring the better humans. We also sketch out some likely ways of improv ing play.
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