ePrints Soton (University of Southampton) · 2008 · 340 citations · 13 references
ComputingComputer EngineeringComputer ScienceAlgorithmsDistributed SystemsMessage PassingDecentralized PlanningDistributed AlgorithmsDistributed ComputingDistributed CoordinationDistributed SensingGraph AlgorithmsBipartite Graph RepresentationDecentralized CommunicationDecentralized Task AllocationFactor Graph OptimizationLow-power Embedded DesignLow-power Embedded Systems
This paper considers the problem of performing decentralised coordination of low-power embedded devices (as is required within many environmental sensing and surveillance applications). Specifically, we address the generic problem of maximising social welfare within a group of interacting agents. We propose a novel representation of the problem, as a cyclic bipartite factor graph, composed of variable and function nodes (representing the agents states and utilities respectively). We show that such representation allows us to use an extension of the max-sum algorithm to generate approximate solutions to this global optimisation problem through local decentralised message passing. We empirically evaluate this approach on a canonical coordination problem (graph colouring), and benchmark it against state of the art approximate and complete algorithms (DSA and DPOP). We show that our approach is robust to lossy communication, that it generates solutions closer to those of DPOP than DSA is able to, and that it does so with a communication cost (in terms of total messages size) that scales very well with the number of agents in the system (compared to the exponential increase of DPOP). Finally, we describe a hardware implementation of our algorithm operating on low-power Chipcon CC2431 System-on-Chip sensor nodes. Copyright © 2008, International Foundation for Autonomous Agents and Multi-agent Systems (www.ifaamas.org). All rights reserved.
13
Information theory, inference, and learning algorithms
Choice Reviews Online · 2004 · 6.6K citations
Analytic and Algorithmic Solution of Random Satisfiability Problems
Marc Mézard, Giorgio Parisi, Riccardo Zecchina · Science · 2002 · 1.1K citations · Full text
Adopt: asynchronous distributed constraint optimization with quality guarantees
Pragnesh Jay Modi, Wei‐Min Shen, Milind Tambe et al. · Artificial Intelligence · 2004 · 762 citations