Improving Performance and Cooperation in Multi-Agent Systems.

Research has shown that evolutionary algorithms are a promising approach for training agents in heterogeneous multi-agent systems. However, research in evolving teams (or ensembles) has proven that common evolutionary approaches have subtle, but significant, weaknesses when it comes to balancing member performance and member cooperation. In addition, there are potentially significant scaling problems in applying evolutionary techniques to very large multi-agent systems. It is impractical to train each member of a large system individually, but purely homogeneous teams are inadequate. Previously we proposed Orthogonal Evolution of Teams (OET) as a novel approach to evolving teams that overcomes the weaknesses with balancing member performance and member cooperation. In this paper we test two basic evolutionary techniques and OET on the problem of evolving multi-agent systems, specifically a landscape exploration problem with heterogeneous agents, and examine the ability of the algorithms to evolve teams that are scalable in the number of team members. Our results confirm that the more traditional evolutionary approaches suffer the same weakness with multi-agent systems as they do with teams and that OET does compensate for these weaknesses. In addition, the three algorithms show distinctly different scaling behavior, with OET scaling significantly better than the two more traditional approaches. [ABSTRACT FROM AUTHOR]