An Experimentally Efficient Method for (MSS, CoMSS) Partitioning

Éric Grégoire, Jean-Marie Lagniez, Bertrand Mazure

AAAI 2014 pp. 2666-2673

doi:10.1609/AAAI.V28I1.9118 /aaai/2014/gregoire2014aaai-experimentally/

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.

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Cite

Text

Grégoire et al. "An Experimentally Efficient Method for (MSS, CoMSS) Partitioning." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9118

Markdown

[Grégoire et al. "An Experimentally Efficient Method for (MSS, CoMSS) Partitioning." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/gregoire2014aaai-experimentally/) doi:10.1609/AAAI.V28I1.9118

BibTeX

@inproceedings{gregoire2014aaai-experimentally,
  title     = {{An Experimentally Efficient Method for (MSS, CoMSS) Partitioning}},
  author    = {Grégoire, Éric and Lagniez, Jean-Marie and Mazure, Bertrand},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {2666-2673},
  doi       = {10.1609/AAAI.V28I1.9118},
  url       = {https://mlanthology.org/aaai/2014/gregoire2014aaai-experimentally/}
}