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Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
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IEEE Transactions on Information Theory
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IEEE Transactions on Information Theory
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Solving a coordination problem in a decentralized environment requires a large amount of resources and thus exploiting the innate system structure and external information as much as possible is necessary for such a problem to be solved in a computationally effective manner. This work proposes new techniques for saving communication and computational resources when solving distributed constraint optimization problems using the Max-Sum algorithm in an environment where system hardware resources are clustered. These techniques facilitate effective problem solving through the use of a pre-computed policy and two phase propagation on Max-Sum algorithm, one inside the clustered resources and one among clustered resources. This approach shows equivalent quality to the standard Max-Sum algorithm while reducing communication requirements on average by50\% and computation resources by 5 to 30\% depending on the specific problem instance. These experiments were performed in a realistic setting involving the scheduling of a network of as many as 192 radars in 48 clusters.