M. Xie (Japan)
autonomous, agent, cooperative, trash collection, genetic algorithm
In autonomous agents systems, each agent must behave independently according to its states and environments, and, if necessary, must cooperate with other agents in order to perform a given task. Therefore, each agent must incorporate learning and evolution in order to adapt to a dynamic environment. At present, in the field of multi-agent systems, methods by which to acquire the behavior rule from both expert knowledge and perception for an autonomous agent are generating a great deal of interest. In the present paper, we focused on the problem of “trash collection” by a multi-agent system and simulated the cooperative behavior of agents. Therefore, we investigated methods by which to learn the rules of cooperative behavior of multi-agents so as to solve problems effectively. We also used genetic algorithms (GA) as a method of acquiring the rules of an agent. Individual coding (definition of the rule) methods are performed, and the learning efficiency is evaluated.
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