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
Preprocessing techniques for accelerating the DCOP algorithm ADOPT
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
No-commitment branch and bound search for distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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
Directed soft arc consistency in pseudo trees
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Journal of Artificial Intelligence Research
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
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
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Soft arc consistency revisited
Artificial Intelligence
BnB-ADOPT+ with Several Soft Arc Consistency Levels
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Maintaining soft arc consistencies in BnB-ADOPT+ during search
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Several multiagent tasks can be formulated and solved as DCOPs. BnB-ADOPT+-AC is one of the most efficient algorithms for optimal DCOP solving. It is based on BnB-ADOPT, removing redundant messages and maintaining soft arc consistency during search. In this paper, we present several improvements for this algorithm, namely (i) a better implementation, (ii) processing exactly simultaneous deletions, and (iii) searching on arc consistent cost functions. We present empirical results showing the benefits of these improvements on several benchmarks.