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A Dynamic Programming-Based MCMC Framework for Solving DCOPs with GPUs

  • Ferdinando FiorettoEmail author
  • William Yeoh
  • Enrico Pontelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9892)

Abstract

The field of Distributed Constraint Optimization (DCOP) has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent coordination. Nevertheless, solving DCOPs is computationally challenging. Thus, in large scale, complex applications, incomplete DCOP algorithms are necessary. Recently, researchers have introduced a promising class of incomplete DCOP algorithms, based on sampling. However, this paradigm requires a multitude of samples to ensure convergence. This paper exploits the property that sampling is amenable to parallelization, and introduces a general framework, called Distributed MCMC (DMCMC), that is based on a dynamic programming procedure and uses Markov Chain Monte Carlo (MCMC) sampling algorithms to solve DCOPs. Additionally, DMCMC harnesses the parallel computing power of Graphical Processing Units (GPUs) to speed-up the sampling process. The experimental results show that DMCMC can find good solutions up to two order of magnitude faster than other incomplete DCOP algorithms.

Keywords

Markov Chain Monte Carlo Proposal Distribution Markov Chain Monte Carlo Algorithm Markov Chain Monte Carlo Sampling Compute Unify Device Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ferdinando Fioretto
    • 1
    • 2
    Email author
  • William Yeoh
    • 1
  • Enrico Pontelli
    • 1
  1. 1.Department of Computer ScienceNew Mexico State UniversityLas CrucesUSA
  2. 2.Department of Mathematics and Computer ScienceUniversity of UdineUdineItaly

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