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Theoretical Computer Science
Volume 349, Issue 2, 14 December 2005, Pages 168-183
Advances in Computer Games
 
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doi:10.1016/j.tcs.2005.09.045    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier B.V. All rights reserved.

Learning to score final positions in the game of Go

Erik C.D. van der WerfCorresponding Author Contact Information, E-mail The Corresponding Author, H. Jaap van den HerikE-mail The Corresponding Author and Jos W.H.M. UiterwijkE-mail The Corresponding Author

Department of Computer Science, Institute for Knowledge and Agent Technology, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands

Available online 10 October 2005.

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Abstract

This article investigates the application of machine-learning techniques for the task of scoring final positions in the game of Go. Neural network classifiers are trained to classify life and death from labelled 9×9 game records. The performance is compared to standard classifiers from statistical pattern recognition. A recursive framework for classification is used to improve performance iteratively. Using a maximum of four iterations our cascaded scoring architecture (CSA*) scores 98.9% of the positions correctly. Nearly all incorrectly scored positions are recognised (they can be corrected by a human operator). By providing reliable score information CSA* opens the large source of Go knowledge implicitly available in human game records for automatic extraction. It thus paves the way for a successful application of machine learning in Go.

Keywords: Go; Learning; Neural net; Scoring; Game records; Life and death


Theoretical Computer Science
Volume 349, Issue 2, 14 December 2005, Pages 168-183
Advances in Computer Games
 
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