Knowledge Compilation with Empowerment

  • Lucas Bordeaux
  • Joao Marques-Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7147)

Abstract

When we encode constraints as Boolean formulas, a natural question is whether the encoding ensures a “propagation completeness” property: is the basic unit propagation mechanism able to deduce all the literals that are logically valid? We consider the problem of automatically finding encodings with this property. Our goal is to compile a naïve definition of a constraint into a good, propagation-complete encoding. Well-known Knowledge Compilation techniques from AI can be used for this purpose, but the constraints for which they can produce a polynomial size encoding are few. We show that the notion of empowerment recently introduced in the SAT literature allows producing encodings that are shorter than with previous techniques, sometimes exponentially.

Keywords

Unit Propagation Constraint Programming Boolean Formula Unit Clause Redundant 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lucas Bordeaux
    • 1
  • Joao Marques-Silva
    • 1
    • 2
    • 3
  1. 1.Microsoft ResearchCambridgeUK
  2. 2.University CollegeDublinIreland
  3. 3.IST/INESC-IDLisbonPortugal

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