Adaptive assessment system for human performance evaluation on game of go

The certificated rank of the human Go player is a number with a high uncertainty so the performance of the human Go player does not always meet the level of the certificated rank. However, the performance of the human Go player, especially for children, may be affected by the on-the-spot environment as well as physical and mental situations of the day. Combined with the technologies of the particle swarm optimization, fuzzy markup language (FML)-based fuzzy inference, and genetic learning algorithm, an adaptive assessment system is presented in this paper to evaluate the performance of the human Go player. The experimental results show the proposed approach is feasible for the application to the adaptive assessment on human Go player´s performance.