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Simulation balancing is a new technique to tune parameters of a playout policy for a Monte-Carlo game-playing program. So far, this algorithm had only been tested in an artificial setting: it was limited to 5 × 5 and 6 × 6 Go, and required a stronger external program that served as a supervisor. In this article, the effectiveness of simulation balancing is demonstrated in a realistic setting. A state-of-the-art program, ERICA, learned an improved playout policy on the 9 × 9 board, without requiring any external expert to provide position evaluations. Evaluations were collected by letting the program analyze positions by itself. This evaluation was run with playout parameters estimated by the minorization-maximization (MM) algorithm. Thanks to simulation balancing, ERICA’s playing strength was improved from a winning rate of 69% (with playout parameters trained by MM) to 78% (with playout parameters trained by SB) against FUEGO0.4.

Move patterns are an essential method to incorporate domain knowledge into Go-playing programs. This article presents a new Bayesian technique for supervised learning of such patterns from game records. The technique is 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. The “Elo ratings” of individual pattern features are computed from these victories, and will 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%). By using these patterns, the 19 × 19 Monte-Carlo program CRAZYSTONEreached the level of the strongest classical programs.