A Learning Method for Dynamics of Multi-Agent System with Mutual Interaction Based on Fuzzy Inference

Abstract

Recently many researches on group robot systems have been studied, where a number of robots behave in a group like birds' or ants. It is generally known that each robot has a limited intellectual power, but the robots can behave more intellectually in a group because they can interact each other. One of the most famous researches in these fields is Boids which is the artificial model of the birds behavior in the computer software. And there have been reported the multi-agent robot systems which can do many kinds of tasks efficiently by training the rules between environments and actions using reinforced learning. This paper also proposes a multi-agent system where a criterion function is defined regarding the behavior of the multi-agent system and parameters of mutual interaction of the agents are trained in order to optimize the above criterion function. From simulations, it has been shown that emergent behaviors of the agents can be developed by appropriately adjusting the parameters.