AbstractW. Yeoh, X. Sun and S. Koenig. Trading Off Solution Quality for Faster Computation in DCOP Search Algorithms. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 354-360, 2009.
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 Uniformly Weighted Heuristics Mechanism generally dominates the other two tradeoff mechanisms for BnB-ADOPT.
Download the paper in pdf.
Many publishers do not want authors to make their papers available electronically after the papers have been published. Please use the electronic versions provided here only if hardcopies are not yet available. If you have comments on any of these papers, please send me an email! Also, please send me your papers if we have common interests.
This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.