Learning Logic Programs Though Divide, Constrain, and Conquer

AAAI 2022 pp. 6446-6453

doi:10.1609/AAAI.V36I6.20596 /aaai/2022/cropper2022aaai-learning/

Abstract

We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search. Our anytime approach can learn optimal, recursive, and large programs and supports predicate invention. Our experiments on three domains (classification, inductive general game playing, and program synthesis) show that our approach can increase predictive accuracies and reduce learning times.

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Cite

Text

Cropper. "Learning Logic Programs Though Divide, Constrain, and Conquer." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I6.20596

Markdown

[Cropper. "Learning Logic Programs Though Divide, Constrain, and Conquer." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/cropper2022aaai-learning/) doi:10.1609/AAAI.V36I6.20596

BibTeX

@inproceedings{cropper2022aaai-learning,
  title     = {{Learning Logic Programs Though Divide, Constrain, and Conquer}},
  author    = {Cropper, Andrew},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {6446-6453},
  doi       = {10.1609/AAAI.V36I6.20596},
  url       = {https://mlanthology.org/aaai/2022/cropper2022aaai-learning/}
}