Handling over-constrained problems in distributed multi-agent systems

Authors:
Lingzhong Zhou;Abdul Sattar;Scott Goodwin
Affiliations:
Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia;Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia;School of Computer Science, University of Windsor, Canada
Venue:
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Year:
2005

Citing 6
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Abstract

The distributed constraint satisfaction problem is a general framework used to represent problems in distributed multi-agent systems In this paper, we describe a detailed investigation of handling over-constrained satisfaction problems in a dynamic and multi-agent environment We introduce a new algorithm, Over-constrained Dynamic Agent Ordering, that treats under and over-constrained problems uniformly While the existing approaches generally only consider a single variable per agent, the proposed algorithm can handle multiple variables per agent In this approach, we use the degree of unsatisfiability as a measure for relaxing constraints, and hence as a way to guide the search towards the best possible solution(s) Through an experimental study, we demonstrate that our algorithm performs better than the one based on asynchronous weak commitment search.