Trading off solution quality for faster computation in DCOP search algorithms

Authors:
William Yeoh;Xiaoxun Sun;Sven Koenig
Affiliations:
Computer Science Department, University of Southern California, Los Angeles, CA;Computer Science Department, University of Southern California, Los Angeles, CA;Computer Science Department, University of Southern California, Los Angeles, CA
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

Citing 12
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Abstract

Distributed Constraint Optimization (DCOP) is a key technique for solving agent coordination problems. Because finding cost-minimal DCOP solutions is NP-hard, it is important to develop mechanisms for DCOP search algorithms that trade off their solution costs for smaller runtimes. However, existing tradeoff mechanisms do not provide relative error bounds. In this paper, we introduce three tradeoff mechanisms that provide such bounds, namely the Relative Error Mechanism, the Uniformly Weighted Heuristics Mechanism and the Non-Uniformly Weighted Heuristics Mechanism, for two DCOP algorithms, namely ADOPT and BnB-ADOPT. Our experimental results show that the Relative Error Mechanism generally dominates the other two tradeoff mechanisms for ADOPT and the UniformlyWeighted Heuristics Mechanism generally dominates the other two trade-off mechanisms for BnB-ADOPT.