Multi-variable agent decomposition for DCOPs

Ferdinando Fioretto, William Yeoh, Enrico Pontelli

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

15 Scopus citations

Abstract

The application of DCOP models to large problems faces two main limitations: (i) Modeling limitations, as each agent can handle only a single variable of the problem; and (ii) Resolution limitations, as current approaches do not exploit the local problem structure within each agent. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decomposition technique, which: (i) Exploits the co-locality of each agent's variables, allowing us to adopt efficient centralized techniques within each agent; (ii) Enables the use of hierarchical parallel models and proposes the use of GPUS; and (iii) Reduces the amount of computation and communication required in several classes of DCOP algorithms.

Original languageEnglish (US)
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI Press
Pages2480-2486
Number of pages7
ISBN (Electronic)9781577357605
StatePublished - Jan 1 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
CountryUnited States
CityPhoenix
Period2/12/162/17/16

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Fioretto, F., Yeoh, W., & Pontelli, E. (2016). Multi-variable agent decomposition for DCOPs. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2480-2486). (30th AAAI Conference on Artificial Intelligence, AAAI 2016). AAAI Press.