Mining Backbone Literals in Incremental SAT

A New Kind of Incremental Data
  • Alexander Ivrii
  • Vadim Ryvchin
  • Ofer StrichmanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9340)


In incremental SAT solving, information gained from previous similar instances has so far been limited to learned clauses that are still relevant, and heuristic information such as activity weights and scores. In most settings in which incremental satisfiability is applied, many of the instances along the sequence of formulas being solved are unsatisfiable. We show that in such cases, with a P-time analysis of the proof, we can compute a set of literals that are logically implied by the next instance. By adding those literals as assumptions, we accelerate the search.


Model Check Bounded Model Check Empty Clause Resolution Graph Resolution Proof 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Alexander Ivrii
    • 1
  • Vadim Ryvchin
    • 2
  • Ofer Strichman
    • 3
    Email author
  1. 1.IBM Research LabHaifaIsrael
  2. 2.Design Technology SolutionsIntel Co.HaifaIsrael
  3. 3.Information Systems Engineering, IETechnionHaifaIsrael

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