Evolving fuzzy logic controllers for multiple mobile robots solving a continuous pursuit problem

Abstract

It is an interesting area in the field of artificial intelligence to find an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One way to overcome this limitation is to implement an evolutionary approach to the design of the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern an emergent co-operative behavior. A modified genetic algorithm is applied to automating the discovery of a fuzzy logic controller for multi-agents playing a pursuit game in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.