Local move prediction in Go

    Erik van der Werf, Jos Uiterwijk, Eric Postma, Jaap van den Herik

    Search and Games Group, IKAT, Department of Computer Science,
    Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands


    The paper presents a system that learns to predict local strong expert moves in the game of Go at a level comparable to that of strong human kyu players. This performance is achieved by four techniques. First, our training algorithm is based on a relative-target approach that avoids needless weight adaptations characteristic of most neural-network classifiers. Second, we reduce dimensionality through state-of-the-art feature extraction, and present two new feature-extraction methods, the Move Pair Analysis and the Modified Eigenspace Separation Transform. Third, informed pre-processing is used to reduce state-space complexity and to focus the feature extraction on important features. Fourth, we introduce and apply second-phase training, i.e., the retraining of the trained network with an augmented input constituting all pre-processed features. Experiments suggest that local move prediction will be a significant factor in enhancing the strength of Go programs.