Emergent Cooperative Strategies for Robot Team Sports

ABSTRACTAutomatic development of cooperative strategies for teams of distributed autonomous robots, or software agents, is presented in this paper. It is shown that a team of robotic agents that initially plays a random game of simulated soccer, can acquire winning strategies through successive generations, utilizing techniques of evolutionary computation. The concept of Tropism-based Control Architecture is introduced that not only allows for the evolution of cooperative strategies, but also obtains the acquired knowledge in a format that is easily comprehensible by humans. The advantage of this approach is that the cooperative strategies can then be transported onto a variety of platforms for testing and deployment. It is discussed as to why the game of robot soccer provides a good environment for this type of investigation, and how the presented concepts can have applications in multi-robot system design. The proposed cognitive architecture has been inspired by biological systems, and the paper include...

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