Multi-Agent Robot Simulation for Evolutionary Learning of Cooperative Behavior

In this research, the evolutionary algorithm is applied to behavior learning of an individual agent in the multi-agent robot system. Each agent robot is given two behavior duties both collision avoidance from the other agent and target (food point) reaching for recovering self-energy. In this paper, we carried out the evolutionary simulation of the cooperative behavior creating an environmental map for the above-mentioned multiagent robots. Each agent robot has two conflicted task, that is, local individual behaviors for selfpreservation or self-protection and global group behaviors for the cooperative task, and has the additional algorithm of the group evolution which the parameters of the best agent are copied to a dead agent, that is, an agent lost its energy. We also report simulation results performed with the evolutionary behavior learning simulator which we developed for multi-agent robots with the map creation task.