@inproceedings{1f22a1570a6844f48bc0c1da2652d32c,
title = "Multi-variable agents decomposition for DCOPs to exploit multi-level parallelism",
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.",
keywords = "DCOP, Distributed constraint optimization, GPGPU",
author = "Ferdinando Fioretto and William Yeoh and Enrico Pontelli",
note = "Publisher Copyright: Copyright {\textcopyright} 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015 ; Conference date: 04-05-2015 Through 08-05-2015",
year = "2015",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "1823--1824",
editor = "Bordini, {Rafael H.} and Pinar Yolum and Edith Elkind and Gerhard Weiss",
booktitle = "AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems",
}