Learning Situation-Specific Coordination in Cooperative Multi-agent Systems

Authors

M. V. Nagendra Prasad, University of Massachusetts - Amherst
Victor R. Lesser, University of Massachusetts - Amherst

Publication Date

1999

Abstract

Achieving effective cooperation in a multi-agent system is a difficult problem for a number of reasons such as limited and possiblyout-dated views of activitiesof 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 learn how to choose the most appropriate coordination strategy from a set of available coordination strategies. COLLAGE relies onmeta-level information about agents’ problemsolving situations to guide themtowards a suitable choice for a coordination strategy. We present empirical results that strongly indicate the effectiveness of the learning algorithm.

Comments

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Recommended Citation

Prasad, M. V. Nagendra and Lesser, Victor R., "Learning Situation-Specific Coordination in Cooperative Multi-agent Systems" (1999). Computer Science Department Faculty Publication Series. 156.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/156