K-means Pattern Learning for Move Evaluation in the Game of Go

摘要

The Game of Go is one of the biggest challenge in the field of Computer Game. The large board makes Go very complex and hard to evaluate. In this paper, we propose a method that reduce the complexity of Go by learning and extracting patterns from game records. This method is more efficient and stronger than the baseline method we have chosen. Our method has two major components: a) a pattern learning method based on K-means, it will learn and extract patterns from game records, b) a perceptron which learns the win rates of Go situations. We build an agent to evaluate the performance of our method, and get at least 20% of performance improvement or 25% of computing power saving in most circumstances.