Uncertainty in Artificial Intelligence
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Bayes' Bluff: Opponent Modelling in Poker
Finnegan Southey, Michael Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, Chris Rayner
Poker is a challenging problem for artificial intelligence, with non-deterministic dynamics, partial observability, and the added difficulty of unknown adversaries. Modelling all of the uncertainties in this domain is not an easy task. In this paper we present a Bayesian probabilistic model for a broad class of poker games, separating the uncertainty in the game dynamics from the uncertainty of the opponent's strategy. We then describe approaches to two key subproblems: (i) inferring a posterior over opponent strategies given a prior distribution and observations of their play, and (ii) playing an appropriate response to that distribution. We demonstrate the overall approach on a reduced version of poker using Dirichlet priors and then on the full game of Texas hold'em using a more informed prior. We demonstrate methods for playing effective responses to the opponent, based on the posterior.
Pages: 550-558
PS Link:
PDF Link: /papers/05/p550-southey.pdf
AUTHOR = "Finnegan Southey and Michael Bowling and Bryce Larson and Carmelo Piccione and Neil Burch and Darse Billings and Chris Rayner",
TITLE = "Bayes' Bluff: Opponent Modelling in Poker",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "550--558"

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