In certain tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called Multi-Agent ESP (Enforced SubPopulations), is proposed and demonstrated in a prey-capture task. First, the approach is shown more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e. through role-based responses to the environment, rather than direct communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multi-agent domains. [ This paper is a revision of AI01-287. ]

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Technical Report AI07-338, Department of Computer Sciences, The University of Texas at Austin, 2007.

@techreport{yong:utcstr07, title={Coevolution of Role-Based Cooperation in Multi-Agent Systems}, author={Chern Han Yong and Risto Miikkulainen}, number={AI07-338}, institution={Department of Computer Sciences, The University of Texas at Austin}, url="http://nn.cs.utexas.edu/?yong:ai07-338", year={2007} }