@article{oai:u-ryukyu.repo.nii.ac.jp:02005222,
 author = {玉城, 清政 and 玉城, 斉 and 山田, 孝治 and 遠藤, 聡志 and Tamashiro, Kiyomasa and Tamaki, Tadashi and Yamada, Koji and Endo, Satoshi},
 issue = {58},
 journal = {琉球大学工学部紀要},
 month = {Sep},
 note = {One of the important issues in intelligent systems and robotics is to develop an efficient method to control multi-agent system. In order to work multi-agent system well as problem solver, it's so significant to create cooperative behaviors among the agents. In the multi-agent system, the behaviors of cooperation emerged as the results of suitable role learning by each agent. In this paper, we evaluate reinforcement learning, genetic algorithm and competitive co-evolution algorithm from the viewpoint of adaptability to different environment as the learning method of multi-agent system, and discuss the property of each technique., 紀要論文},
 pages = {143--149},
 title = {マルチエージェント系における競合共進化型学習の性能評価},
 year = {1999}
}