Learning Organizational Roles in a Heterogeneous Multi-agent System
Munaga V. N. K. Prasad, Victor Lesser, Sarah Lander · 1995
Previous work in self-organization for efficient distributed search control has, for the most part, involved simple agents with simple interaction patterns. The work presented in this paper represents one of the few attempts at demonstrating 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 in negotiated search for mutually acceptable designs. We tested the system on a steam condenser design domain and empirically demonstrated its usefulness. L-TEAM produced better results than its non-learning predecessor, 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 learning issues in multi-agent systems.