Learning MDL logic programs from noisy data

Céline Hocquette, Andreas Niskanen, Matti Järvisalo, Andrew Cropper
8/18/2023
cs.LGcs.LO

Official code: celinehocquette/aaai24-maxsynth (7 stars, last commit 2024-02-16).

Abstract

Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.

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Code Implementations(1)

celinehocquette/aaai24-maxsynthOfficial

C. Hocquette, A. Niskanen, M Järvisalo, and Andrew Cropper, Learning MDL Logic Programs From Noisy Data, AAAI24.

71Perl, PrologFeb 16, 20242 years ago
inductive-logic-programmingprogram-synthesis

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Cite this paper

@article{hocquette2023learning,
  title  = {Learning MDL logic programs from noisy data},
  author = {Céline Hocquette and Andreas Niskanen and Matti Järvisalo and Andrew Cropper},
  year   = {2023},
  eprint = {2308.09393},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.09393v1}
}

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