Solutions for Hard and Soft Constraints Using Optimized Probabilistic Satisfiability

  • Marcelo Finger
  • Ronan Le Bras
  • Carla P. Gomes
  • Bart Selman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7962)

Abstract

Practical problems often combine real-world hard constraints with soft constraints involving preferences, uncertainties or flexible requirements. A probability distribution over the models that meet the hard constraints is an answer to such problems that is in the spirit of incorporating soft constraints.

We propose a method using SAT-based reasoning, probabilistic reasoning and linear programming that computes such a distribution when soft constraints are interpreted as constraints whose violation is bound by a given probability. The method, called Optimized Probabilistic Satisfiability (oPSAT), consists of a two-phase computation of a probability distribution over the set of valuations of a SAT formula. Algorithms for both phases are presented and their complexity is discussed.

We also describe an application of the oPSAT technique to the problem of combinatorial materials discovery.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marcelo Finger
    • 1
  • Ronan Le Bras
    • 1
  • Carla P. Gomes
    • 1
  • Bart Selman
    • 1
  1. 1.Department of Computer ScienceCornell UniversityUSA

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