Reinforcement distribution for fuzzy classifiers: a methodology to extend crisp algorithms

Fuzzy classifier systems (FCS) implement a mapping from real numbers to real numbers, through fuzzy interpretation of input and output. Reinforcement learning (RL) algorithms can be successfully applied to develop learning FCS analogously to what can be done with learning classifier systems (LCS). The author motivates this approach and presents a methodology to extend straightforwardly reinforcement distribution algorithms originally designed for crisp input and output to fully exploit the features of FCS.