Inductive Logic Programming at 30

Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton

MLJ 2022 pp. 147-172

doi:10.1007/S10994-021-06089-1 /mlj/2022/cropper2022mlj-inductive/

Abstract

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

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Text

Cropper et al. "Inductive Logic Programming at 30." Machine Learning, 2022. doi:10.1007/S10994-021-06089-1

Markdown

[Cropper et al. "Inductive Logic Programming at 30." Machine Learning, 2022.](https://mlanthology.org/mlj/2022/cropper2022mlj-inductive/) doi:10.1007/S10994-021-06089-1

BibTeX

@article{cropper2022mlj-inductive,
  title     = {{Inductive Logic Programming at 30}},
  author    = {Cropper, Andrew and Dumancic, Sebastijan and Evans, Richard and Muggleton, Stephen H.},
  journal   = {Machine Learning},
  year      = {2022},
  pages     = {147-172},
  doi       = {10.1007/S10994-021-06089-1},
  volume    = {111},
  url       = {https://mlanthology.org/mlj/2022/cropper2022mlj-inductive/}
}