Learning in Multi-Agent Information Systems - A Survey from IS Perspective

Multiagent systems (MAS), long studied in artificial intelligence, have recently become popular in mainstream IS research. This resurgence in MAS research can be attributed to two phenomena: the spread of concurrent and distributed computing with the advent of the web; and a deeper integration of computing into organizations and the lives of people, which has led to increasing collaborations among large collections of interacting people and large groups of interacting machines. However, it is next to impossible to correctly and completely specify these systems a priori, especially in complex environments. The only feasible way of coping with this problem is to endow the agents with learning, i.e., an ability to improve their individual and/or system performance with time. Learning in MAS has therefore become one of the important areas of research within MAS. In this paper we present a survey of important contributions made by IS researchers to the field of learning in MAS, and present directions for future research in this area

Learning Cases to Resolve Conflicts and Improve Group Behavior

Groups of agents following fixed behavioral rules can be limited in performance and efficiency. 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. In particular, we propose a framework in which individual group members learn cases to improve their model of other group members. We use 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 agent groups following static behavioral rules. 1 Introduction An agent is defined to be rational if when faced with a choice from a set of actions, it chooses the one that maximizes the expected utilities of those actions [19]. Implicit in this definition is the assumption that the preference o..

Learning Cases to Resolve Conflicts and Improve Group Behavior

Groups of agents following fixed behavioral rules can be limited in performance and efficiency. 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. In particular, we propose a framework in which individual group members learn cases from problem-solving experiences to improve their model of other group members. We use 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 agent groups following static behavioral rules