Rémi Coulom
Université Charles de Gaulle, INRIA SEQUEL, CNRS GRAPPA, Lille, France
Abstract. Move patterns are an essential method to incorporate do-
main 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 victo-
ries, and can be used in previously unseen positions to compute a prob-
ability distribution over legal moves. In this approach, several pattern
features may be combined, without an exponential cost in the number
of features. [...]