Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
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
Max-sum decentralised coordination for sensor systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
Decentralised coordination of continuously valued control parameters using the max-sum algorithm
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Asynchronous forward bounding for distributed COPs
Journal of Artificial Intelligence Research
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
Artificial Intelligence - Special issue: Distributed constraint satisfaction
When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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
Improving DPOP with function filtering
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Decentralized Coordination in RoboCup Rescue
The Computer Journal
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Autonomous Agents and Multi-Agent Systems
Quality guarantees for region optimal DCOP algorithms
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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Distributed Constraint Optimization Problems (DCOPs) are NP-hard and therefore the number of studies that consider incomplete algorithms for solving them is growing. Specifically, the Max-sum algorithm has drawn attention in recent years and has been applied to a number of realistic applications. Unfortunately, in many cases Max-sum does not produce high quality solutions. More specifically, when problems include cycles of various sizes in the factor graph upon which Max-sum performs, the algorithm does not converge and the states that it visits are of low quality. In this paper we advance the research on incomplete algorithms for DCOPs by: (1) Proposing a version of the Max-sum algorithm that operates on an alternating directed acyclic graph (Max-sum_AD), which guarantees convergence in linear time. (2) Identifying major weaknesses of Max-sum and Max-sum_AD that cause inconsistent costs/utilities to be propagated and affect the assignment selection. (3) Solving the identified problems by introducing value propagation to Max-sum_AD. Our empirical study reveals a large improvement in the quality of the solutions produced by Max-sum_AD with value propagation (VP), when solving problems which include cycles, compared with the solutions produced by the standard Max-sum algorithm, Bounded Max-sum and Max-sum_AD with no value propagation.