Improving (Revolutionary Search for Optimal Multiagent Behaviors

Evolutionary computation is a useful technique for learning behaviors in multiagent systems. Among the several types of evolutionary computation, one natural and popular method is to coevolve multiagent behaviors in multiple, cooperating populations. Recenl research has suggested that r e v o lutionary systems may favor stability rather than performance in some domains. In order to improve upon existing methods, this paper examines the idea of modifying traditional coevolution, biasing it to search for maximal rewards. We introduce a theoretical justification of the improved method and present experiments in three problem domains. We conclude that biasing can help coevolution find better results in some multiagent problem domains.