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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/814

Title: Impact of problem centralization in distributed constraint optimization algorithms
Authors: Davin, John
Modi, Pragnesh Jay
Issue Date: 2005
Publisher: IEEE
Citation: Proceedings of Autonomous Agents and Multi-Agent Systems, (AAMAS) 2005. Retrieved 3/16/2006 from http://www.cs.drexel.edu/~pmodi/papers/modi-aamas05a.pdf.
Abstract: Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the dominant evaluation metric of “number of cycles” fails to account for this cost. We analyze the relative performance of two recent algorithms for DCOP: OptAPO, which performs partial centralization, and Adopt, which maintains distribution of the DCOP. Previous comparison of Adopt and OptAPO has found that OptAPO requires fewer cycles than Adopt. We extend the cycles metric to define “Cycle-Based Runtime (CBR)” to account for both the amount of computation required in each cycle and the communication latency between cycles. Using the CBR metric, we show that Adopt outperforms OptAPO under a range of communication latencies. We also ask: What level of centralization is most suitable for a given communication latency? We use CBR to create performance curves for three algorithms that vary in degree of centralization, namely Adopt, OptAPO, and centralized Branch and Bound search.
URI: http://hdl.handle.net/1860/814
Appears in Collections:Faculty Research and Publications (Comp Sci)

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