Genetic fuzzy markup language for game of NoGo

摘要

NoGo is similar to the game of Go in terms of gameplay; however, the goal is different: the first player who either suicides or kills a group loses the game and the first player with no legal move loses the game. In this paper, we propose an approach combining the technologies of ontologies, evolutionary computation, fuzzy logic, and fuzzy markup language (FML) with a genetic algorithm (GA)-based system for the NoGo game. Based on the collected patterns and the pre-constructed fuzzy NoGo ontology, the genetic FML (GFML) with the fuzzy inference mechanism is able to analyze the situation of the current game board and then play next move to an inferred good-move position. Additionally, the genetic learning mechanism continuously evolves the adopted GFMLs to enable an increase in the winning rate of the GA-based NoGo via playing with the baseline NoGo. In the proposed approach, first, the domain experts construct the important NoGo patterns and the fuzzy NoGo ontology based on the rules of NoGo and the past game records. Second, each GA-based NoGo as White plays against the baseline NoGo as Black according to the inferred and calculated good-move position, respectively. Third, the genetic learning mechanism is carried out to generate two new evolved GFMLs and then the worst two GFMLs stored in the GFML repository are replaced. Fourth, the GFML with the highest winning rate is randomly sampled from the GFML repository in the time series. Finally, one by one the GA-based NoGo adopts the sampled GFML to play lots of games against the baseline NoGo to obtain the winning rate of the GA-based NoGo. The acquired winning rates at the time series show that the proposed approach can work effectively and that the average winning rate of the GA-based NoGo program is much stronger than the baseline NoGo program.