Meta-Interpretive Learning Using HEX-Programs

Tobias Kaminski, Thomas Eiter, Katsumi Inoue

IJCAI 2019 pp. 6186-6190

doi:10.24963/IJCAI.2019/860 /ijcai/2019/kaminski2019ijcai-meta/

Abstract

Meta-Interpretive Learning (MIL) is a recent approach for Inductive Logic Programming (ILP) implemented in Prolog. Alternatively, MIL-problems can be solved by using Answer Set Programming (ASP), which may result in performance gains due to efficient conflict propagation. However, a straightforward MIL-encoding results in a huge size of the ground program and search space. To address these challenges, we encode MIL in the HEX-extension of ASP, which mitigates grounding issues, and we develop novel pruning techniques.

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Cite

Text

Kaminski et al. "Meta-Interpretive Learning Using HEX-Programs." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/860

Markdown

[Kaminski et al. "Meta-Interpretive Learning Using HEX-Programs." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/kaminski2019ijcai-meta/) doi:10.24963/IJCAI.2019/860

BibTeX

@inproceedings{kaminski2019ijcai-meta,
  title     = {{Meta-Interpretive Learning Using HEX-Programs}},
  author    = {Kaminski, Tobias and Eiter, Thomas and Inoue, Katsumi},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {6186-6190},
  doi       = {10.24963/IJCAI.2019/860},
  url       = {https://mlanthology.org/ijcai/2019/kaminski2019ijcai-meta/}
}