New CNF Features and Formula Classification

15 pages•Published: July 28, 2014

Abstract

In this paper we first present three new features for classifying CNF formulas. These features are based on the structural information of the formula and consider AND-gates as well as exactly-one constraints. Next, we use these features to construct a machine learning approach to select a SAT solver configuration for CNF formulas with random decision forests. Based on this classification task we can show that our new features are useful compared to existing features. Since the computation time for these features is small, the constructed classifier improves the performance of the SAT solvers on application and hand crafted benchmarks. On the other hand, the comparison shows that the set of new features also results in a better classification.

Keyphrases: cnf formulas, feature extraction, machine learning, sat solving

In: Daniel Le Berre (editor). POS-14. Fifth Pragmatics of SAT workshop, vol 27, pages 57-71.

Links:	https://easychair.org/publications/paper/xw2
	https://doi.org/10.29007/b8t1

BibTeX entry

@inproceedings{POS-14:New_CNF_Features_Formula,
  author    = {Enrique Alfonso and Norbert Manthey},
  title     = {New CNF Features and Formula Classification},
  booktitle = {POS-14. Fifth Pragmatics of SAT workshop},
  editor    = {Daniel Le Berre},
  series    = {EPiC Series in Computing},
  volume    = {27},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/xw2},
  doi       = {10.29007/b8t1},
  pages     = {57-71},
  year      = {2014}}

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