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Bryce Larson

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2 papers

2 author rows

UAI Conference 2005 Conference Paper

Bayes? Bluff: Opponent Modelling in Poker

Finnegan Southey
Michael H. Bowling
Bryce Larson
Carmelo Piccione
Neil Burch
Darse Billings
D. 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.

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NeurIPS Conference 2004 Conference Paper

Maximum Margin Clustering

Linli Xu
James Neufeld
Bryce Larson
Dale Schuurmans

We propose a new method for clustering based on finding maximum mar- gin hyperplanes through data. By reformulating the problem in terms of the implied equivalence relation matrix, we can pose the problem as a convex integer program. Although this still yields a difficult com- putational problem, the hard-clustering constraints can be relaxed to a soft-clustering formulation which can be feasibly solved with a semidef- inite program. Since our clustering technique only depends on the data through the kernel matrix, we can easily achieve nonlinear clusterings in the same manner as spectral clustering. Experimental results show that our maximum margin clustering technique often obtains more accurate results than conventional clustering methods. The real benefit of our ap- proach, however, is that it leads naturally to a semi-supervised training method for support vector machines. By maximizing the margin simul- taneously on labeled and unlabeled training data, we achieve state of the art performance by using a single, integrated learning principle.

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