Comparison of Bayesian move prediction systems for Computer Go

Abstract

Since the early days of research on Computer Go, move prediction systems are an important building block for Go playing programs. Only recently, with the rise of Monte Carlo Tree Search (MCTS) algorithms, the strength of Computer Go programs increased immensely while move prediction remains to be an integral part of state of the art programs. In this paper we review three Bayesian move prediction systems that have been published in recent years and empirically compare them under equal conditions. Our experiments reveal that, given identical input data, the three systems can achieve almost identical prediction rates while differing substantially in their needs for computational and memory resources. From the analysis of our results, we are able to further improve the prediction rates for all three systems.