Temporal difference learning and TD-Gammon
Communications of the ACM
Journal of the ACM (JACM)
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Static analysis of life and death in the game of Go
Information Sciences—Informatics and Computer Science: An International Journal
Computer Go: an AI oriented survey
Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Honte, a go-playing program using neural nets
Machines that learn to play games
The Golem Go Program
Learning to predict life and death from Go game records
Information Sciences: an International Journal
Learning to estimate potential territory in the game of go
CG'04 Proceedings of the 4th international conference on Computers and Games
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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.