Learning Behaviors represented as Fuzzy Logic Controllers

1. Introduction The implementation of artificial autonomous agent behaviors as Fuzzy Logic Controllers (FLC) has natural and engineering motivations. Fuzzy logic is recognized as a powerful mean to represent approximation intrinsic in human and animal reasoning and reacting. On the other side, fuzzy logic shows flexibility and robustness, important in the implementation of artificial devices. Learning FLC makes possible the adaptation of the agent to the environment, and saves design time and efforts. In this paper, we present Behavioral Engineering (BE) issues, focusing on the role of learning as a support to this new branch of engineering. We discuss issues related to learn behaviors as FLC, and we propose our approach implemented in ELF (Evolutionary Learning for Fuzzy rules). We are using ELF to support the development of different types of agents. We also discuss architectural issues to combine behaviors. Finally, we present the results we obtained both in simulated and real environments.