Soccer-playing robots can develop skills based on the success or failure of previous behavior, and skill-development is enhanced when all team members adopt successful behavior. ABSTRACT | Coevolution has been receiving increased attention as a method for simultaneously developing the control structures of multiple agents. Our ultimate goal is the mutual development of skills through coevolution. The coevolutionary process is, however, often prone to settle into suboptimal strategies. The key

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... successful coevolution has thus far been unclear. This paper discusses how several robots can emerge cooperative and competitive behavior through coevolutionary processes. In order to realize successful coevolution, we propose two ideas: multiple schedules for incremental evolution and fitness sharing based on the method of importance sampling. To examine this issue, we conducted a series of computer simulations. We have chosen a simplified soccer game consisting of two or three robots as a testbed for analyzing a problem in which both competitive and cooperative tasks are involved. We show that the proposed fitness evaluation allows robots to evolve robust behaviors in cooperative and competitive situations.