Learning to predict life and death from Go game records
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.
learning life and death go game records neural net