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
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
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
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
Risk-neutral bounded max-sum for distributed constraint optimization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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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 moderately dense problems, in this paper we show that its theoretical approximation ratio is overestimated, thus overshadowing its good performance. We propose a new algorithm, called Improved Bounded Max-Sum, whose approximate solutions are at least as good as the ones found by Bounded Max-Sum and with a tighter approximation ratio. Our empirical evaluation shows that the new approximation ratio is significantly tighter.