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H-FQL: A new reinforcement learning method for automatic hierarchization of fuzzy systems: An application to the route choice problem

This paper proposes a new approach of automatic hierarchization for monolithic fuzzy systems based on an extension of the fuzzy Q-learning method. This approach contributes to the reduction of the fuzzy rules base without recourse to expert knowledge. It suggests firstly a new technique of automatic structural hierarchization, which advocates the association of the most correlated input variables' pairs through the statistical study of the samples' base. It also proposes the auto-generation of rules' bases using an adaptation of the Fuzzy Q-Learning (FQL) to the Hierarchical Fuzzy Systems (HFS). Finally, we applied the proposed approach to hierarchize an adaptive monolithic fuzzy system dealing with the route choice problem.