Learning organizational roles for negotiated search in a multiagent system
✍ Scribed by M.V.N. PRASAD; VICTOR R. LESSER; SUSAN E. LANDER
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 379 KB
- Volume
- 48
- Category
- Article
- ISSN
- 1071-5819
No coin nor oath required. For personal study only.
✦ Synopsis
This paper presents studies in learning a form of organizational knowledge called organizational roles in a multi-agent agent system. It attempts 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 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.