Building a computer Mahjong player based on Monte Carlo simulation and opponent models

Predicting opponents´ moves and hidden states is important in imperfect information games. This paper describes a method for building a Mahjong program that models opponent players and performs Monte Carlo simulation with the models. We decompose an opponent´s play into three elements, namely, waiting, winning tiles, and winning scores, and train prediction models for those elements using game records of expert human players. Opponents´ moves in the Monte Carlo simulations are determined based on the probability distributions of the opponent models. We have evaluated the playing strength of the resulting program on a popular online Mahjong site “Tenhou”. The program has achieved a rating of 1718, which is significantly higher than that of the average human player.