Multi-Agent Coordination: DCOPs and Beyond
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2838-2838.
Abstract
Distributed constraint optimization problems (DCOPs) are a model for representing multi-agent systems in which agents cooperate to optimize a global objective. The DCOP model has two main advantages: it can represent a wide range of problem domains, and it supports the development of generic algorithms to solve them. Firstly, this paper presents some advances in both complete and approximate DCOP algorithms. Secondly, it explains that the DCOP model makes a number of unrealistic assumptions that severely limit its range of application. Finally, it points out hints on how to tackle such limitations.
@inproceedings{ijcai2011p491,
title = {Multi-Agent Coordination: DCOPs and Beyond},
author = {Pujol-Gonzalez, Marc},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {2838--2838},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-491},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-491},
}
TY - CPAPER TI - Multi-Agent Coordination: DCOPs and Beyond AU - Pujol-Gonzalez, Marc T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 2838 EP - 2838 DO - 10.5591/978-1-57735-516-8/IJCAI11-491 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-491 ER -Copied
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