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Reformulating Constraint Satisfaction Problems to Improve Scalability

  • Kenneth M. Bayer
  • Martin Michalowski
  • Berthe Y. Choueiry
  • Craig A. Knoblock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4612)

Abstract

Constraint Programming is a powerful approach for modeling and solving many combinatorial problems, scalability, however, remains an issue in practice. Abstraction and reformulation techniques are often sought to overcome the complexity barrier. In this paper we introduce four reformulation techniques that operate on the various components of a Constraint Satisfaction Problem (CSP) in order to reduce the cost of problem solving and facilitate scalability. Our reformulations modify one or more component of the CSP (i.e., the query, variables domains, constraints) and detect symmetrical solutions to avoid generating them. We describe each of these reformulations in the context of CSPs, then evaluate their performance and effects in on the building identification problem introduced by Michalowski and Knoblock [1].

Keywords

Bipartite Graph Constraint Satisfaction Problem Maximum Matchings Resource Allocation Problem Oriented Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kenneth M. Bayer
    • 1
  • Martin Michalowski
    • 2
  • Berthe Y. Choueiry
    • 1
    • 2
  • Craig A. Knoblock
    • 2
  1. 1.Constraint Systems Laboratory, University of Nebraska-Lincoln 
  2. 2.University of Southern California, Information Sciences Institute 

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