SIMULATION FOR BEHAVIOR LEARNING OF MULTIAGENT ROBOT

In our research, the evolutionary algorithm is applied to behavior lea rning of an individual agent in multi-agent robots. Each robot, which is an agent, is given two behavior duties, collision avoidance from ot her agents and target (food point) reaching for recovering self-energy . Addressing the problem of two conflicting behaviors, collision avoid ance and target reaching motion of multi-agent robots, the learning me thod to change the self-energy and the behavior gain of each agent is discussed in this paper. Each agent has the same rules and is controll ed as a homogeneous distributed system without any central or hierarch ical control. Furthermore, we perform a simulation with the additional algorithm of a group evolution in which the parameters of the most ex cellent agent are copied to a dead agent, that is, an agent that has l ost its energy. The simulation confirmed that each agent has the abili ties of behavior learning and group evolution.