TY - GEN
T1 - Multi-variable agent decomposition for DCOPs
AU - Fioretto, Ferdinando
AU - Yeoh, William
AU - Pontelli, Enrico
N1 - Funding Information:
This research is partially supported by NSF grants 1345232, 0947465, and 1540168. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government.
Publisher Copyright:
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85007203237
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 2480
EP - 2486
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
PB - AAAI Press
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -