Computing "Elo Ratings" of Move Patterns in the Game of Go

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. 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 19◊ 19 Monte-Carlo program improved with these patterns reached the level of the strongest classical programs. and little domain expertise. This paper presents a new supervised pattern-learning algorithm, based on the Bradley-Terry model. The Bradley-Terry model is the theoretical basis of the Elo rating system. The principle of Elo ratings, as applied to chess, is that each player gets a numerical strength estimation, computed from the observation of past game results. From the ratings of players, it is possible to estimate a probability distribution over the outcome of future games. The same principle