TY - GEN

T1 - Reducing the search space of resource constrained DCOPs

AU - Matsui, Toshihiro

AU - Silaghi, Marius

AU - Hirayama, Katsutoshi

AU - Yokoo, Makoto

AU - Faltings, Boi

AU - Matsuo, Hiroshi

PY - 2011

Y1 - 2011

N2 - Distributed constraint optimization problems (DCOPs) have been studied as a basic framework of multi-agent cooperation. The Resource Constrained DCOP (RCDCOP) is a special DCOP framework that contains n-ary hard constraints for shared resources. In RCDCOPs, for a value of a variable, a certain amount of the resource is consumed. Upper limits on the total use of resources are defined by n-ary resource constraints. To solve RCDCOPs, exact algorithms based on pseudo-trees employ virtual variables whose values represent use of the resources. Although, virtual variables allow for solving the problems without increasing the depth of the pseudo-tree, they exponentially increase the size of search spaces. Here, we reduce the search space of RCDCOPs solved by a dynamic programming method. Several boundaries of resource use are exploitable to reduce the size of the tables. To employ the boundaries, additional pre-processing and further filtering are applied. As a result, infeasible solutions are removed from the tables. Moreover, multiple elements of the tables are aggregated into fewer elements. By these modifications, redundancy of the search space is removed. One of our techniques reduces the size of the messages by an order of magnitude.

AB - Distributed constraint optimization problems (DCOPs) have been studied as a basic framework of multi-agent cooperation. The Resource Constrained DCOP (RCDCOP) is a special DCOP framework that contains n-ary hard constraints for shared resources. In RCDCOPs, for a value of a variable, a certain amount of the resource is consumed. Upper limits on the total use of resources are defined by n-ary resource constraints. To solve RCDCOPs, exact algorithms based on pseudo-trees employ virtual variables whose values represent use of the resources. Although, virtual variables allow for solving the problems without increasing the depth of the pseudo-tree, they exponentially increase the size of search spaces. Here, we reduce the search space of RCDCOPs solved by a dynamic programming method. Several boundaries of resource use are exploitable to reduce the size of the tables. To employ the boundaries, additional pre-processing and further filtering are applied. As a result, infeasible solutions are removed from the tables. Moreover, multiple elements of the tables are aggregated into fewer elements. By these modifications, redundancy of the search space is removed. One of our techniques reduces the size of the messages by an order of magnitude.

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U2 - 10.1007/978-3-642-23786-7_44

DO - 10.1007/978-3-642-23786-7_44

M3 - Conference contribution

AN - SCOPUS:80053032603

SN - 9783642237850

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 576

EP - 590

BT - Principles and Practice of Constraint Programming, CP 2011 - 17th International Conference, Proceedings

T2 - 17th International Conference on Principles and Practice of Constraint Programming, CP 2011

Y2 - 12 September 2011 through 16 September 2011

ER -