  Poker is ideal for testing automated reasoning under uncertainty. It
introduces uncertainty both by physical randomization and by incomplete
information about opponents hands.Another source OF uncertainty IS the limited
information available TO construct psychological models OF opponents, their
tendencies TO bluff, play conservatively, reveal weakness, etc. AND the
relation BETWEEN their hand strengths AND betting behaviour. 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 describe our Bayesian Poker
Program(BPP), which uses a Bayesian network TO model the programs poker hand,
the opponents hand AND the opponents playing behaviour conditioned upon the
hand, and betting curves which govern play given a probability of winning. The
history of play with opponents is used to improve BPPs understanding OF their
behaviour.We compare BPP experimentally WITH : a simple RULE - based system; a
program which depends exclusively ON hand probabilities(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 improving play.
