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arXiv · 1207.1411

Bayes' Bluff: Opponent Modelling in Poker

Finnegan Southey·Michael P. Bowling·Bryce Larson·Carmelo Piccione·Neil Burch·Darse Billings·Chris Rayner
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Abstract

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.

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Finnegan Southey, Michael P. Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, Chris Rayner. 2012-07-04. Bayes' Bluff: Opponent Modelling in Poker. https://arxiv.org/abs/1207.1411

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