Immune based fuzzy agent plays checkers game

One of the advantages of immune based approaches is the usage of permanent memory cells. These memory cells cause to omit the process of learning for any played strategy and consequently increasing the speed of decision making process. In the proposed method of this article, memory cells represent actions that have the best local payoff for that current state of the game and are generated simultaneously by learning process. These cells help the decision making system to decide better, considering the previous and future state of the game. The decision making system that is used in this method is based on a Mamdani fuzzy inference engine (FIS). The FIS proposes a best action for the current state of the board by extracting memory cells’ data. Experiments show that the immune based fuzzy agent which is introduced here has better results among other previous methods. This new method can show proper resistance when confronting a player that uses complete game tree remarkably. Also this method is capable of suggesting an action for each state of the game by generating less number of generations in comparison with other evolutionary based methods.

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► Usage of permanent memory cells in algorithms based on immune system.
► Memory cells are represented as actions that have the best local payoff for each current state of the game.
► Decision making system that is used in this method is based on a Mamdani fuzzy inference engine (FIS).
► Fuzzy rules are extracted from memory cells in decision making phase.
► The method is capable of suggesting an action for each state of the game in less generation.