Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Sensor networks and distributed CSP: communication, computation and complexity
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving
Artificial Intelligence - Special issue: Distributed constraint satisfaction
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Distributed constraint optimization problems related with soft arc consistency
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Including soft global constraints in DCOPs
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Maintaining soft arc consistencies in BnB-ADOPT+ during search
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Protecting privacy through distributed computation in multi-agent decision making
Journal of Artificial Intelligence Research
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Distributed constraint optimization problems can be solved by BnB-ADOPT+, a distributed asynchronous search algorithm. In the centralized case, local consistency techniques applied to constraint optimization have been shown very beneficial to increase performance. In this paper, we combine BnB-ADOPT+ with different levels of soft arc consistency, propagating unconditional deletions caused by either the enforced local consistency or by distributed search. The new algorithm maintains BnB-ADOPT+ optimality and termination. In practice, this approach decreases substantially BnB-ADOPT+ requirements in communication cost and computation effort when solving commonly used benchmarks.