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Learning a Go Heuristic with Tilde

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2063)

Abstract

In Go, an important factor that hinders search is the large branching factor, even in local problems. Human players are strong at recognizing frequently occurring shapes and vital points. This allows them to select the most promising moves and to prune the search tree. In this paper we argue that many of these shapes can be represented as relational concepts. We present an application of the relational learner TILDE in which we learn a heuristic that gives values to candidate-moves in tsume-go (life and death) problems. Such a heuristic can be used to limit the number of evaluated moves. Even if all moves are evaluated, alpha-beta search can be sped up considerably when the candidate-moves are approximately ordered from good to bad.We validate our approach with experiments and analysis.

Keywords

  • Machine Learning
  • Go
  • Decision trees
  • Inductive Logic Programming
  • Tsume-Go

Acknowledgements

Jan Ramon is supported by the Flemish Institute for the Promotion of Science and Technological Research in Industry (IWT). Hendrik Blockeel is a post-doctoral fellow of the Fund for Scientific Research (FWO) of Flanders. We thank Thomas Wolf for making available his dataset of tsume-go problems. We also thank the reviewers for their comments and Maurice Bruynooghe and Johannes Fürnkranz for proofreading the paper.

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Ramon, J., Francis, T., Blockeel, H. (2001). Learning a Go Heuristic with Tilde . In: Marsland, T., Frank, I. (eds) Computers and Games. CG 2000. Lecture Notes in Computer Science, vol 2063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45579-5_10

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  • DOI: https://doi.org/10.1007/3-540-45579-5_10

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