Reducing the search space of resource constrained DCOPs

  • Authors:

  • Toshihiro Matsui;Marius Silaghi;Katsutoshi Hirayama;Makoto Yokoo;Boi Faltings;Hiroshi Matsuo

  • Affiliations:

  • Nagoya Institute of Technology, Nagoya, Japan;Florida Institute of Technology, Melbourne FL;Kobe University, Kobe, Japan;Kyushu University, Fukuoka, Japan;Swiss Federal Institute of Technology, Lausanne, Lausanne, Switzerland;Nagoya Institute of Technology, Nagoya, Japan

  • Venue:
  • CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 pseudotrees 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.