Towards Smarter MACE-style Model Finders

17 pages•Published: October 23, 2018

Abstract

Finite model finders represent a powerful tool for deciding problems with the finite model property, such as the Bernays-Sch ̈onfinkel fragment (EPR). Further, finite model finders provide useful information for counter-satisfiable conjectures. The paper investigates several novel techniques in a finite model-finder based on the translation to SAT, referred to as the MACE-style approach. The approach we propose is driven by counterexample abstraction refinement (CEGAR), which has proven to be a powerful tool in the context of quantifiers in satisfiability modulo theories (SMT) and quantified Boolean formulas (QBF).
One weakness of CEGAR-based approaches is that certain amount of luck is required in order to guess the right model, because the solver always operates on incomplete information about the formula. To tackle this issue, we propose to enhance the model finder with a machine learning algorithm to improve the likelihood that the right model is encountered. The implemented prototype based on the presented ideas shows highly promising results.

Keyphrases: cegar, epr, finite model finder, sat

In: Gilles Barthe, Geoff Sutcliffe and Margus Veanes (editors). LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning, vol 57, pages 454-470.

Links:	https://easychair.org/publications/paper/rZKt
	https://doi.org/10.29007/w42s

BibTeX entry

@inproceedings{LPAR-22:Towards_Smarter_MACE_style,
  author    = {Mikolas Janota and Martin Suda},
  title     = {Towards Smarter MACE-style Model Finders},
  booktitle = {LPAR-22. 22nd International Conference on Logic for Programming, Artificial Intelligence and Reasoning},
  editor    = {Gilles Barthe and Geoff Sutcliffe and Margus Veanes},
  series    = {EPiC Series in Computing},
  volume    = {57},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/rZKt},
  doi       = {10.29007/w42s},
  pages     = {454-470},
  year      = {2018}}

Download PDF Open PDF in browser