LEARNING ORGANIZATIONAL ROLES FOR NEGOTIATED SEARCH IN A MULTIAGENT SYSTEM

This paper presents studies in learning a form of organizational knowl edge called organizational roles in a multi-agent agent system. It att empts to demonstrate the viability and utility of self-organization in an agent-based system involving complex interactions within the agent set. We present a multi-agent parametric design system called L-TEAM where a set of heterogeneous agents learn their organizational roles i n negotiated search for mutually acceptable designs. We tested the sys tem on a steam condenser design domain and empirically demonstrated it s usefulness. L-TEAM produced better results than its non-learning pre decessor, TEAM, which required elaborate knowledge engineering to hand -code organizational roles for its agent set. In addition, we discuss experiments with L-TEAM that highlight the importance of certain learn ing issues in multi-agent systems. (C) 1998 Academic Press Limited.