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Revisiting SATZilla Features in 2024

Authors Hadar Shavit , Holger H. Hoos

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Subject Classification

ACM Subject Classification
  • Theory of computation → Logic and verification
Keywords
  • Satisfiability
  • feature extraction
  • running time prediction
  • satisfiability prediction

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Abstract

Boolean satisfiability (SAT) is an NP-complete problem with important applications, notably in hardware and software verification. Characterising a SAT instance by a set of features has shown great potential for various tasks, ranging from algorithm selection to benchmark generation. In this work, we revisit the widely used SATZilla features and introduce a new version of the tool used to compute them. In particular, we utilise a new preprocessor and SAT solvers, adjust the code to accommodate larger formulas, and determine better settings of the feature extraction time limits. We evaluate the extracted features on three downstream tasks: satisfiability prediction, running time prediction, and algorithm selection. We observe that our new tool is able to extract features from a broader range of instances than before. We show that the new version of the feature extractor produces features that achieve up to 26% lower RMSE for running time prediction, up to 3% higher accuracy for satisfiability prediction, and up to 15 times higher closed gap for algorithm selection on benchmarks from recent SAT competitions.

Cite As Get BibTex

Hadar Shavit and Holger H. Hoos. Revisiting SATZilla Features in 2024. In 27th International Conference on Theory and Applications of Satisfiability Testing (SAT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 305, pp. 27:1-27:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.SAT.2024.27

Author Details

Hadar Shavit
  • Chair for AI Methodology, RWTH Aachen University, Germany
Holger H. Hoos
  • Chair for AI Methodology, RWTH Aachen University, Germany
  • Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands
  • Department of Computer Science, University of British Columbia, Vancouver, Canada

Funding

This research was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation program under GA No. 952215. HH gratefully acknowledges support through an Alexander-von-Humboldt Professorship in Artificial Intelligence.

Acknowledgements

We would like to thank Matthias König and Anja Jankovic for the discussions. We would also like to thank the reviewers who gave a valuable feedback.

Supplementary Materials

References

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