Learning Cases to Compliment Rules for Conflict Resolution in Multiagent Systems
Thomas D. Haynes, Kit Fun Lau, Sandip Sen · 1996
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.