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Conference Papers Year : 2007

Computing Elo Ratings of Move Patterns in the Game of Go

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Rémi Coulom
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Abstract

Move patterns are an essential method to incorporate domain knowledge into Go-playing programs. This paper presents a new Bayesian technique for supervised learning of such patterns from game records, based on a generalization of Elo ratings. Each sample move in the training data is considered as a victory of a team of pattern features. Elo ratings of individual pattern features are computed from these victories, and can be used in previously unseen positions to compute a probability distribution over legal moves. In this approach, several pattern features may be combined, without an exponential cost in the number of features. Despite a very small number of training games (652), this algorithm outperforms most previous pattern-learning algorithms, both in terms of mean log-evidence (−2.69), and prediction rate (34.9%). A 19x19 Monte-Carlo program improved with these patterns reached the level of the strongest classical programs.
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Dates and versions

inria-00149859 , version 1 (29-05-2007)

Identifiers

  • HAL Id : inria-00149859 , version 1

Cite

Rémi Coulom. Computing Elo Ratings of Move Patterns in the Game of Go. Computer Games Workshop, Jun 2007, Amsterdam, Netherlands. ⟨inria-00149859⟩
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