Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
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In this paper, we argue that the insertion of domain knowledge into ensemble of diverse evolutionary checkers can produce improved strategies and reduce evolution time by restricting search space. The evolutionary approach for game is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages, so that it is not sometimes efficient to evolve simple heuristic that is found easily by humans because it is based purely on a bottom-up style of construction. In this paper, we have proposed the systematic insertion of opening knowledge and an endgame database into the framework of evolutionary checkers. Also, common knowledge, the combination of diverse strategies is better than the single best one, is inserted into the middle stage and is implemented using crowding algorithm and a strategy combination scheme. Experimental results show that the proposed method is promising for generating better strategies.