An Experimentally Efficient Method for (MSS,CoMSS) Partitioning

Authors

  • Eric Grégoire Artois University
  • Jean-Marie Lagniez Artois University
  • Bertrand Mazure Artois University

DOI:

https://doi.org/10.1609/aaai.v28i1.9118

Keywords:

SAT, coMSS, MSS

Abstract

The concepts of MSS (Maximal Satisfiable Subset) andCoMSS (also called Minimal Correction Subset) playa key role in many A.I. approaches and techniques. Inthis paper, a novel algorithm for partitioning a BooleanCNF formula into one MSS and the correspondingCoMSS is introduced. Extensive empirical evaluationshows that it is more robust and more efficient on mostinstances than currently available techniques.

AAAI 2014 Proceedings Cover

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Published

2014-06-21

How to Cite

Grégoire, E., Lagniez, J.-M., & Mazure, B. (2014). An Experimentally Efficient Method for (MSS,CoMSS) Partitioning. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.9118

Issue

Vol. 28 No. 1 (2014): Twenty-Eighth AAAI Conference on Artificial Intelligence

Section

Main Track: Search and Constraint Satisfaction