Computing Elo Ratings of Move Patterns in the Game of GoReport as inadecuate

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1 SEQUEL - Sequential Learning LIFL - Laboratoire d-Informatique Fondamentale de Lille, LAGIS - Laboratoire d-Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe

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.

Author: Rémi Coulom -



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