Learning Cases to Compliment Rules for Conflict Resolution in Multiagent Systems

Groups of agents following fixed behavioral rules can be limited in performance and etficiency. Adaptability and flexibility are key components of intelligent behav- ior which allow agent groups to improve performance in a given domain using prior problem solving experi- ence. We motivate the usefulness of individual learn- ing by group members in the context of overall group behavior. We propose a framework in which individ- ual group members learn cases to improve their model of other group members. We utilize a testbed prob- lem from the distributed AI literature to show that simultaneous learning by group members can lead to significant improvement in group performance and ef- ficiency over groups following static behavioral rules.