by Rémi Coulom. Accepted at the Computer Games Workshop 2007, Amsterdam, The Netherlands.
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.
Here you can download a GTP engine, for Windows and Linux. It is designed to work with GoGui's analyze feature, as shown in the screenshot below.
You can also download the C++ source code of the MM implementation I used for Crazy Stone: