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A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection

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Book cover Logic Programming and Nonmonotonic Reasoning (LPNMR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13416))

Abstract

Answer set programming (ASP) has long been used for modeling and solving hard search problems. Experience shows that the performance of ASP tools on different ASP encodings of the same problem may vary greatly from instance to instance and it is rarely the case that one encoding outperforms all others. We describe a system and its implementation that given a set of encodings and a training set of instances, builds performance models for the encodings, predicts the execution time of these encodings on new instances, and uses these predictions to select an encoding for solving.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Graceful_labeling.

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Acknowledgments

The authors acknowledge the support of the NSF grant IIS 1707371.

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Authors and Affiliations

  1. University of Kentucky, Lexington, KY, 40506, USA

    Liu Liu & Mirek Truszczynski

  2. University of Nebraska at Omaha, Omaha, NE, 68182, USA

    Yuliya Lierler

Authors
  1. Liu Liu
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  2. Mirek Truszczynski
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  3. Yuliya Lierler
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Corresponding author

Correspondence to Mirek Truszczynski .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Georg Gottlob

  2. Miami University, Oxford, OH, USA

    Daniela Inclezan

  3. University of Genova, Genoa, Italy

    Marco Maratea

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Liu, L., Truszczynski, M., Lierler, Y. (2022). A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection. In: Gottlob, G., Inclezan, D., Maratea, M. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2022. Lecture Notes in Computer Science(), vol 13416. Springer, Cham. https://doi.org/10.1007/978-3-031-15707-3_32

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  • DOI: https://doi.org/10.1007/978-3-031-15707-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15706-6

  • Online ISBN: 978-3-031-15707-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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