Learning behaviors implemented as Fuzzy Logic Controllers for Autonomous Agents

The implementation of behaviors for embodied autonomous agents by means of 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. Two aspects of the development of autonomous agents may be faced by learning FLCs: the adaptation of the agent to the environment, and the reduction of the design time and efforts. In this paper, we present issues related to learn behaviors implemented as FLCs, 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. Finally, we present the results that we have obtained both in simulated and real environments.