Learning organizational roles for negotiated search in a multiagent system

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 m...

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Bibliographic Details
Published in:International journal of human-computer studies 1998-01, Vol.48 (1), p.51-67
Main Authors: PRASAD, M.V.N., LESSER, VICTOR R., LANDER, SUSAN E.
Format: Article
Language:English
Subjects:
Computer applications
Engineering
Machine learning
Multiagents
Organizational roles
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Summary: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.
ISSN:1071-5819
1095-9300
DOI:10.1006/ijhc.1997.0160