Copyright © 2005 Elsevier B.V. All rights reserved.
Learning to score final positions in the game of Go
Available online 10 October 2005.
References and further reading may be available for this article. To view references and further reading you must purchase this article.
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






E-mail Article
Add to my Quick Links

Cited By in Scopus (0)






