Incremental DCOP search algorithms for solving dynamic DCOPs

Authors:
William Yeoh;Pradeep Varakantham;Xiaoxun Sun;Sven Koenig
Affiliations:
University of Massachusetts Amherst, MA;Singapore Management University, Singapore;University of Southern California Los Angeles, CA;University of Southern California Los Angeles, CA
Venue:
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Year:
2011

Citing 3
Cited 1

Caching schemes for DCOP search algorithms

Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Adopt: asynchronous distributed constraint optimization with quality guarantees

Artificial Intelligence - Special issue: Distributed constraint satisfaction
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm

Journal of Artificial Intelligence Research

Target to sensor allocation: A hierarchical dynamic Distributed Constraint Optimization approach

Computer Communications

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Abstract

Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we model dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by any-space ADOPT and any-space BnB-ADOPT to find cost-minimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper bounds from the previous DCOP when solving the next one in the sequence. Our experimental results show that the speedup gained from this procedure increases with the amount of memory the agents have available.