Associating Shallow and Selective Global Tree Search with Monte Carlo for 9 × 9 Go

Abstract

This paper explores the association of shallow and selective global tree search with Monte Carlo in 9 × 9 Go. This exploration is based on Olga and Indigo, two experimental Monte-Carlo programs. We provide a min-max algorithm that iteratively deepens the tree until one move at the root is proved to be superior to the other ones. At each iteration, random games are started at leaf nodes to compute mean values. The progressive pruning rule and the min-max rule are applied to non terminal nodes. We set up experiments demonstrating the relevance of this approach. Indigo used this algorithm at the 8th Computer Olympiad held in Graz.