Bayes’ Bluff: Opponent Modelling in Poker
Finnegan Southey, Michael Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, Chris Rayner
Department of Computing Science
University of Alberta
Edmonton, Alberta, Canada T6G 2E8

Abstract:
Poker is a challenging problem for articial intelligence, with non-deterministic dynamics, partial observability, and the added difculty 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.

PDF available for download here: http://www.cs.ualberta.ca/~bowling/papers/05uai.pdf