Reinforcement learning for games: failures and successes
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Immune based fuzzy agent plays checkers game
Applied Soft Computing
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The evolutionary approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is, therefore, sometimes inefficient to evolve simple heuristics that may be created easily by humans because it is based purely on a bottom-up style of construction. Incorporating domain knowledge into evolutionary computation can improve the performance of evolved strategies and accelerate the speed of evolution by reducing the search space. In this paper, we propose the systematic insertion of opening knowledge and an endgame database into the framework of evolutionary checkers. Also, the common knowledge that the combination of diverse strategies is better than a single best one is included in 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.