Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10325205 | Information Sciences | 2005 | 15 Pages |
Abstract
This article presents a new learning system for predicting life and death in the game of Go. It is called Gone. The system uses a multi-layer perceptron classifier which is trained on learning examples extracted from game records. Blocks of stones are represented by a large amount of features which enable a rather precise prediction of life and death. On average, Gone correctly predicts life and death for 88% of all the blocks that are relevant for scoring. Towards the end of a game the performance increases up to 99%. A straightforward extension for full-board evaluation is discussed. Experiments indicate that the predictor is an important component for building a strong full-board evaluation function.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Erik C.D. van der Werf, Mark H.M. Winands, H. Jaap van den Herik, Jos W.H.M. Uiterwijk,