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Learning on Graphs in the Game of Go

  • Thore Graepel
  • Mike Goutrié
  • Marco Krüger
  • Ralf Herbrich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2130)

Abstract

We consider the game of Go from the point of view of machine learning and as a well-defined domain for learning on graph representations. We discuss the representation of both board positions and candidate moves and introduce the common fate graph (CFG) as an adequate representation of board positions for learning. Single candidate moves are represented as feature vectors with features given by subgraphs relative to the given move in the CFG. Using this representation we train a support vector machine (SVM) and a kernel perceptron to discriminate good moves from bad moves on a collection of life-and-death problems and on 9 × 9 game records. We thus obtain kernel machines that solve Go problems and play 9 × 9 Go.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Thore Graepel
    • 1
  • Mike Goutrié
    • 1
  • Marco Krüger
    • 1
  • Ralf Herbrich
    • 1
  1. 1.Computer Science DepartmentTechnical University of BerlinBerlinGermany

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