Publication - Learning Situation Specific Coordination in Cooperative Multi-Agent Systems

Authors: Nagendra Prasad, M.V. and Lesser, V
Title: Learning Situation Specific Coordination in Cooperative Multi-Agent Systems
Abstract: Achieving effective cooperation in a multi-agent system is a difficult problem for a number of reasons such as limited and possibly out-dated views of activities of other agents and uncertainty about the outcomes of interacting non-local tasks. In this paper, we present a learning system called COLLAGE, that endows the agents with the capability to lean how to choose the most appropriate coordination strategy form a set of available coordination strategies. COLLAGE relies on meta-level information about agentsí problem solving situations to guide them towards a suitable choice for a coordination strategy. We present empirical results that strongly indicate the effectiveness of the learning algorithm.
Publication: Autonomous Agents and Multi-Agent Systems, Vol: 2, pp. 173 - 207
Publisher: Kluwer Academic Publishers
Date: 1999
Sources: PDF: /Documents/Nagendra/prasad99learning.pdf
Reference: Nagendra Prasad, M.V. and Lesser, V. Learning Situation Specific Coordination in Cooperative Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems, Volume 2, Kluwer Academic Publishers, pp. 173-207. 1999.
bibtex:
@article{Nagendra-274,
  author    = "M.V. Nagendra Prasad and V Lesser",
  title     = "{Learning Situation Specific Coordination in
               Cooperative Multi-Agent Systems}",
  journal   = "Autonomous Agents and Multi-Agent Systems",
  volume    = "2",
  publisher = "Kluwer Academic Publishers",
  pages     = "173-207",
  year      = "1999",
  url       = "http://mas.cs.umass.edu/paper/274",
}