Multi-variable agents decomposition for DCOPs to exploit multi-level parallelism

Ferdinando Fioretto, William Yeoh, Enrico Pontelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Cuffent DCOP algorithms suffer from a major limiting assumption-each agent can handle only a single variable of the problem-which limits their scalability. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decomposition, which: (i) Exploits co-locality of an agent's variables, allowing us to adopt efficient centralized techniques; (ii) Enables the use of hierarchical parallel models, such us those based on GPGPUs; and (iii) Empirically reduces the amount of communication required in several classes of DCOP algorithms. Experimental results show that our MVA decomposition outperforms non-decomposed DCOP algorithms, in terms of network load and scalability.

Original languageEnglish (US)
Title of host publicationAAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems
EditorsRafael H. Bordini, Pinar Yolum, Edith Elkind, Gerhard Weiss
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1823-1824
Number of pages2
ISBN (Electronic)9781450337717
StatePublished - 2015
Event14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015 - Istanbul, Turkey
Duration: May 4 2015May 8 2015

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015
CountryTurkey
CityIstanbul
Period5/4/155/8/15

Keywords

  • DCOP
  • Distributed constraint optimization
  • GPGPU

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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