Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
DCOPolis: a framework for simulating and deploying distributed constraint reasoning algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
Adaptive price update in distributed Lagrangian relaxation protocol
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Asynchronous algorithms for approximate distributed constraint optimization with quality bounds
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Divide-and-coordinate: DCOPs by agreement
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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This paper presents a new DCOP algorithm called DeQED (Decomposition with Quadratic Encoding to Decentralize). DeQED is based on the Divide-and-Coordinate (DaC) framework, where the agents repeatedly solve their updated local sub-problems (the divide stage) and exchange coordination information that causes them to update their local sub-problems (the coordinate stage). Unlike other DaC-based DCOP algorithms, DeQED does not essentially increase the complexity of local subproblems and allows agents to avoid exchanging (primal) variable values in the coordinate stage. Our experimental results show that DeQED significantly outperformed other incomplete DCOP algorithms for both random and structured instances.