Bayes' Bluff: Opponent Modelling in Poker

Finnegan Southey, Michael Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, and Chris Rayner. Bayes' Bluff: Opponent Modelling in Poker. In Proceedings of the Twenty-First Conference on Uncertaintyin Artificial Intelligence (UAI), pp. 550–558, 2005.


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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.


  Title = "Bayes' Bluff: {O}pponent Modelling in Poker",
  Author = "Finnegan Southey and Michael Bowling and Bryce Larson and Carmelo Piccione and Neil Burch and Darse Billings and Chris Rayner",
  Booktitle = "Proceedings of the Twenty-First Conference on Uncertaintyin Artificial Intelligence (UAI)",
  Pages = "550--558",
  Year = "2005",
  AcceptRate = "34\%",
  AcceptNumbers = "83 of 243"

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