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K-means Pattern Learning for Move Evaluation in the Game of Go

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PRICAI 2014: Trends in Artificial Intelligence (PRICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8862))

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Abstract

The Game of Go is one of the biggest challenge in the field of Computer Game. The large board makes Go very complex and hard to evaluate. In this paper, we propose a method that reduce the complexity of Go by learning and extracting patterns from game records. This method is more efficient and stronger than the baseline method we have chosen. Our method has two major components: a) a pattern learning method based on K-means, it will learn and extract patterns from game records, b) a perceptron which learns the win rates of Go situations. We build an agent to evaluate the performance of our method, and get at least 20% of performance improvement or 25% of computing power saving in most circumstances.

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Author information

Authors and Affiliations

  1. Beijing Institute of Technology, China

    Yunzhao Liang & Shuoying Chen

Authors
  1. Yunzhao Liang
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  2. Shuoying Chen
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Editor information

Editors and Affiliations

  1. MIMOS Berhad Technology Park Malaysia, 57000, Bukit Jalil, KL, Malaysia

    Duc-Nghia Pham

  2. Kyungpook National University, Sankyuk-Dong, Buk-Gu, 702-701, Daegu, Korea

    Seong-Bae Park

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Liang, Y., Chen, S. (2014). K-means Pattern Learning for Move Evaluation in the Game of Go. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_39

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  • DOI: https://doi.org/10.1007/978-3-319-13560-1_39

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13559-5

  • Online ISBN: 978-3-319-13560-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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