Examination of Indicators for Estimating Players' Strength by Using Computer Go

Abstract

This study proposes a method to estimate players' skill using Computer Go. Computer Go has been considered one of the biggest challenges of artificial intelligence (AI) research. The AI of Go is based on the Monte Carlo tree search (MCTS) algorithm unlike the games of chess and shogi which are based on the game tree search using the evaluation function. Further, we apply the evaluation index used in the strength estimation method for shogi to the game of Go. We analyze the game records of KGS and YUUGEN-NO-MA with the evaluation index using the MCTS winning rate. It is concluded that stronger AI is necessary for identifying strength-estimating indicators.