Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Evaluating the performance of DCOP algorithms in a real world, dynamic problem
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
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
Anytime local search for distributed constraint optimization
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Adaptive price update in distributed Lagrangian relaxation protocol
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Agent-organized networks for multi-agent production and exchange
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Quality guarantees on k-optimal solutions for distributed constraint optimization problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The role of clustering on the emergence of efficient social conventions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Egalitarian Utilities Divide-and-Coordinate: Stop arguing about decisions, let's share rewards!
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Quality guarantees for region optimal DCOP algorithms
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
SBDO: a new robust approach to dynamic distributed constraint optimisation
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Multi-agent coordination: dcops and beyond
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
DeQED: an efficient divide-and-coordinate algorithm for DCOP
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
DeQED: an efficient divide-and-coordinate algorithm for DCOP
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper we investigate an approach to provide approximate, anytime algorithms for DCOPs that can provide quality guarantees. At this aim, we propose the divide-and-coordinate (DaC) approach. Such approach amounts to solving a DCOP by iterating (1) a divide stage in which agents divide the DCOP into a set of simpler local subproblems and solve them; and (2) a coordinate stage in which agents exchange local information that brings them closer to an agreement. Next, we formulate a novel algorithm, the Divide and Coordinate Subgradient Algorithm (DaCSA), a computational realization of DaC based on Lagrangian decompositions and the dual subgradient method. By relying on the DaC approach, DaCSA provides bounded approximate solutions. We empirically evaluate DaCSA showing that it is competitive with other state-of-the-art DCOP approximate algorithms and can eventually outper-form them while providing useful quality guarantees.