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
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
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
Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decentralized Coordination in RoboCup Rescue
The Computer Journal
Autonomous Agents and Multi-Agent Systems
Faster Algorithms for Max-Product Message-Passing
The Journal of Machine Learning Research
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
AINTEC'06 Proceedings of the Second Asian international conference on Technologies for Advanced Heterogeneous Networks
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
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Many distributed constraint optimization (DCOP) algorithms include nodes' local maximization operation that searches for the optimal variable assignment in a limited context. When the variable domain is discrete, this operation is exponential in the number of associated variables and thus computationally challenging. McAuley's recent work on efficient inference implements this maximization operator such that in most cases only a small set of values is examined without loss of accuracy. We increase the applicability of such approach to DCOP in the three following ways. First, we extend it to non-pairwise graphs with better computational expected complexity. Second, we remove the requirement for offline sorting, which often is not realistic in many DCOP domains, while keeping the same complexity. Third, we provide a correlation measure to determine dynamically the appropriate cases to apply the technique since its efficiency is sensitive to characteristics of the data sets. We combine this technique with the Max-Sum algorithm and verify empirically that our approach provides significant time savings over the standard Max-Sum algorithm.