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