Multi-Variable Agents Decomposition for DCOPs
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 withineach agent. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decompositiontechnique, 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.
Cite
Text
Fioretto et al. "Multi-Variable Agents Decomposition for DCOPs." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10127Markdown
[Fioretto et al. "Multi-Variable Agents Decomposition for DCOPs." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/fioretto2016aaai-multi/) doi:10.1609/AAAI.V30I1.10127BibTeX
@inproceedings{fioretto2016aaai-multi,
title = {{Multi-Variable Agents Decomposition for DCOPs}},
author = {Fioretto, Ferdinando and Yeoh, William and Pontelli, Enrico},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {2480-2486},
doi = {10.1609/AAAI.V30I1.10127},
url = {https://mlanthology.org/aaai/2016/fioretto2016aaai-multi/}
}