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
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
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
Quality guarantees on k-optimal solutions for distributed constraint optimization problems
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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
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Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
Self-organising sensors for wide area surveillance using the max-sum algorithm
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Quality guarantees for region optimal DCOP algorithms
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IEEE Transactions on Information Theory
Improved bounded max-sum for distributed constraint optimization
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Bounded Max-Sum is a message-passing algorithm for solving Distributed Constraint Optimization Problems able to compute solutions with a guaranteed approximation ratio. Although its approximate solutions were empirically proved to be within a small percentage of the optimal solution on low and moderate dense problems, in this paper we show that a simple modification systematically provides even better solutions. This is especially relevant in critical applications (e.g. disaster response scenarios) where the accuracy of solutions is of vital importance.