This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q( )-learning with function approximation (fuzzy inference system) is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to three different pursuit-evasion differential games. The proposed technique is compared with the classical control strategy, Q( )-learning only, and the technique proposed by Dai et al. (2005) in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed technique.

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This paper addresses the problem of tuning fuzzy logic controllers. In this paper we presents a new technique called a genetic based fuzzy logic controller (GBFLC). The proposed technique is used to iteratively tune the set of fuzzy logic controller parameters such as membership functions and scaling factors. The proposed technique is also used to reduce the number of fuzzy rules. Computer simulations are performed on a wall-following mobile robot and the results show the usefulness of the proposed technique.