Efficient Learning of Logic Programs with Non-Determinant, Non-Discriminating Literals
Abstract
We propose a new heuristic-based approach to Horn-clause logic program learning. The heuristic can be viewed as an improvement in the information-based estimate employed in FOIL, and captures the important goal-directed usefulness of a literal which is overlooked in it. Our system, CHAM, learns a class of complex programs not learned by previous systems, i.e., non-determinate programs out of the learning space of GOLEM, and programs with non-discriminating literals which pose difficulties for FOIL. While being able to learn the larger class of programs, CHAM is shown to preserve efficiency in various test problems.
Cite
Text
Kijsirikul et al. "Efficient Learning of Logic Programs with Non-Determinant, Non-Discriminating Literals." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50086-6Markdown
[Kijsirikul et al. "Efficient Learning of Logic Programs with Non-Determinant, Non-Discriminating Literals." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/kijsirikul1991icml-efficient/) doi:10.1016/B978-1-55860-200-7.50086-6BibTeX
@inproceedings{kijsirikul1991icml-efficient,
title = {{Efficient Learning of Logic Programs with Non-Determinant, Non-Discriminating Literals}},
author = {Kijsirikul, Boonserm and Numao, Masayuki and Shimura, Masamichi},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {417-421},
doi = {10.1016/B978-1-55860-200-7.50086-6},
url = {https://mlanthology.org/icml/1991/kijsirikul1991icml-efficient/}
}