Agent Technology

Learning to Score Final Positions in the Game of Go

Machine learning systems can achieve near-perfect accuracy in determining the final scoring of Go games by classifying the life-and-death status of stone blocks.

• Machine learning models achieve 98.9% accuracy on 9x9 Go boards and 99.4% on 19x19 boards by classifying the life-and-death status of stone blocks.
• The system utilizes a recursive partition neural network trained on human game records to replace static knowledge bases with adaptive, learned representations.
• Models trained exclusively on 9x9 board configurations demonstrate high performance on 19x19 boards, proving the methodology is effective at generalizing across different board sizes without additional training.