Convex Optimization
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
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Solution sets for DCOPs and graphical games
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On k-optimal distributed constraint optimization algorithms: new bounds and algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Anytime local search for distributed constraint optimization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Distributed Constraint Optimization for Large Teams of Mobile Sensing Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Trading off solution quality for faster computation in DCOP search algorithms
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Divide-and-coordinate: DCOPs by agreement
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Local search for distributed asymmetric optimization
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Balancing local resources and global goals in multiply-constrained DCOP
Multiagent and Grid Systems
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Two decades of multiagent teamwork research: past, present, and future
CARE@AI'09/CARE@IAT'10 Proceedings of the CARE@AI 2009 and CARE@IAT 2010 international conference on Collaborative agents - research and development
Quality guarantees for region optimal DCOP algorithms
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Pseudo-tree-based incomplete algorithm for distributed constraint optimization with quality bounds
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Effective Variants of the Max-Sum Algorithm for Radar Coordination and Scheduling
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
The Knowledge Engineering Review
Max/min-sum distributed constraint optimization through value propagation on an alternating DAG
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Risk-neutral bounded max-sum for distributed constraint optimization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Mitigating multi-path fading in a mobile mesh network
Ad Hoc Networks
Asymmetric distributed constraint optimization problems
Journal of Artificial Intelligence Research
Improving the privacy of the asynchronous partial overlay protocol
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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A distributed constraint optimization problem (DCOP) is a formalism that captures the rewards and costs of local interactions within a team of agents. Because complete algorithms to solve DCOPs are unsuitable for some dynamic or anytime domains, researchers have explored incomplete DCOP algorithms that result in locally optimal solutions. One type of categorization of such algorithms, and the solutions they produce, is k- optimality; a k-optimal solution is one that cannot be improved by any deviation by k or fewer agents. This paper presents the first known guarantees on solution quality for k-optimal solutions. The guarantees are independent of the costs and rewards in the DCOP, and once computed can be used for any DCOP of a given constraint graph structure.