Biasing Coevolutionary Search for Optimal Multiagent Behaviors

Liviu Panait, Sean Luke, R. Paul Wiegand
2006 IEEE Transactions on Evolutionary Computation  
Cooperative coevolutionary algorithms offer great potential for concurrent multiagent learning domains and are of special utility to domains involving teams of multiple agents. Unfortunately, they also exhibit pathologies resulting from their game-theoretic nature, and these pathologies interfere with finding solutions that correspond to optimal collaborations of interacting agents. We address this problem by biasing a cooperative coevolutionary algorithm in such a way that the fitness of an
more » ... ividual is based partly on the result of interactions with other individuals (as is usual), and partly on an estimate of the best possible reward for that individual if partnered with its optimal collaborator. We justify this idea using existing theoretical models of a relevant subclass of coevolutionary algorithms, demonstrate how to apply biasing in a way that is robust with respect to parameterization, and provide some experimental evidence to validate the biasing approach. We show that it is possible to bias coevolutionary methods to better search for optimal multiagent behaviors.
doi:10.1109/tevc.2006.880330 fatcat:5a7c7tukr5eu5atx2jxl6d7wpm