Computing Elo Ratings of Move Patterns in the Game of Go

Rémi Coulom 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
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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Communication dans un congrès
van den Herik, H. Jaap and Mark Winands and Jos Uiterwijk and Maarten Schadd. Computer Games Workshop, Jun 2007, Amsterdam, Netherlands. 2007
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https://hal.inria.fr/inria-00149859
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Soumis le : mardi 29 mai 2007 - 10:44:26
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
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Rémi Coulom. Computing Elo Ratings of Move Patterns in the Game of Go. van den Herik, H. Jaap and Mark Winands and Jos Uiterwijk and Maarten Schadd. Computer Games Workshop, Jun 2007, Amsterdam, Netherlands. 2007. 〈inria-00149859〉

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