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T2FML-based adaptive assessment system for computer game of Go

Go game is one game with a long history. Two players alternatively play their black or white stone at a vacant intersection of the board. Usually, weaker player holds Black. In the end, the player with bigger territory wins the game. For the learning in Go games, humans could be advanced to higher rank, for example, by winning 4 out of 5 games. However, a Go player's performance could be influenced by some factors, such as the spot environment as well as physical and mental situations of the day, which causes the difficulty and uncertainty in certificating the human's rank. In this paper, a type-2 fuzzy markup language (T2FML)-based system is proposed to infer the human's rank according to simulation number, komi, and board size. Based on the adaptive Upper Confidence Bounds for Trees (UCT)-based Go-ranking mechanism, the number of simulations for each move of the game is collected when the invited Go players are against the computer Go program, MoGoTW. At the same time, the strength of the human is also estimated by using the Bradley-Terry and Particle Swarm Optimization (PSO) models. The experimental results show that the proposed approach is feasible for estimating the strength of a human.