Behavior learning and group evolution for autonomous multi-agent robot

In this research, the evolutionary algorithm is applied to behavior learning of an individual agent in multi-agent robots. Each robot which is an agent is given two behavior duties both collision avoidance from the other agent and target (food point) reaching for recovering self-energy. In the problem for two conflicting behaviors, collision avoidance and target reaching motion, of multi-agent robots the learning method of behavior based on the self-energy and the behavior gain of each agent was discussed in the author´s previous paper (1996). In this paper, he performs the simulation with the additional algorithm of the group evolution which the parameters of the most excellent agent are copied to a dead agent, that is, an agent lost its energy. It was confirmed in the simulation that each agent has abilities of both behavior learning and group evolution