Publication - Learning Organizational Roles for Negotiatiated Search in a MultiAgent System

Authors: Nagendra Prasad, M.V.,Lesser,V.R. and Lander, S.
Title: Learning Organizational Roles for Negotiatiated Search in a MultiAgent System
Abstract: 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. ( 1998 Academic Press Limited)
Keywords: Cooperative Negotiation, Distributed Search, Learning, Multi-Agent Systems, Negotiation, Organizational Design
Publisher: International Journal of Human-Computer Studies
Date: January 1998
Sources: PDF: /Documents/Nagendra_Learning_1998.pdf
Reference: Nagendra Prasad, M.V.,Lesser,V.R. and Lander, S.. Learning Organizational Roles for Negotiatiated Search in a MultiAgent System. January 1998.
bibtex: 
@article{Prasad-103,
  author    = "M.V. Nagendra Prasad and V.R. Lesser and S. Lander",
  title     = "{Learning Organizational Roles for Negotiatiated
               Search in a MultiAgent System}",
  volume    = "48",
  publisher = "International Journal of Human-Computer Studies",
  pages     = "51-67",
  month     = "January",
  year      = "1998",
  url       = "http://mas.cs.umass.edu/paper/103",
}