Fuzzy motivations in behavior based agents

Abstract

In this chapter we describe a fuzzy logic based approach for providing biologically based motivations to be used by agents in evolutionary behavior learning. In this approach, fuzzy logic provides a fitness measure used in the generation of agents with complex behaviors which respond to user expectations of previously specified motivations. Our approach is implemented in behavior based navigation, route planning and action sequence based environment recognition tasks in a Khepera mobile robot simulator. Our fuzzy logic based motivation technique is shown as a simple and powerful method for agents to acquire a diverse set of fit behaviors as well as providing an intuitive user interface framework. © 2010 Springer-Verlag Berlin Heidelberg.