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Assumption-Based Pruning in Conditional CSP

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Part of the Lecture Notes in Computer Science book series (LNPSE,volume 3709)

Abstract

A conditional constraint satisfaction problem (CCSP) is a variant of the standard constraint satisfaction problem (CSP). CCSPs model problems where some of the variables and constraints may be conditionally inactive such that they do not participate in a solution. Recently, algorithms were introduced that use MAC at their core to solve CCSP. We extend MAC with a simple assumption-based reasoning. The resulting algorithm, Activity MAC (AMAC), is able to achieve significantly better pruning than existing methods. AMAC is shown to be more than two orders of magnitude more efficient than CondMAC on certain problem classes. Our algorithm is most naturally expressed using a variant of the CCSP representation that we refer to as Activity CSP (ACSP). ACSP introduces activity variables which explicitly control the presence of other variables in the solution. Common aspects of CCSP, such as activity clustering and disjunction, are easily captured by ACSP and contribute to improved pruning by AMAC.

Keywords

  • Activity Variable
  • Constraint Satisfaction Problem
  • Activity Constraint
  • Activity Cluster
  • Compatibility Constraint

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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© 2005 Springer-Verlag Berlin Heidelberg

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Geller, F., Veksler, M. (2005). Assumption-Based Pruning in Conditional CSP. In: van Beek, P. (eds) Principles and Practice of Constraint Programming - CP 2005. CP 2005. Lecture Notes in Computer Science, vol 3709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564751_20

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  • DOI: https://doi.org/10.1007/11564751_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29238-8

  • Online ISBN: 978-3-540-32050-0

  • eBook Packages: Computer ScienceComputer Science (R0)