Learning Higher-Order Logic Programs from Failures
Abstract
Learning complex programs through inductive logic programming (ILP) remains a formidable challenge. Existing higher-order enabled ILP systems show improved accuracy and learning performance, though remain hampered by the limitations of the underlying learning mechanism. Experimental results show that our extension of the versatile Learning From Failures paradigm by higher-order definitions significantly improves learning performance without the burdensome human guidance required by existing systems. Our theoretical framework captures a class of higher-order definitions preserving soundness of existing subsumption-based pruning methods.
Cite
Text
Purgal et al. "Learning Higher-Order Logic Programs from Failures." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/378Markdown
[Purgal et al. "Learning Higher-Order Logic Programs from Failures." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/purgal2022ijcai-learning/) doi:10.24963/IJCAI.2022/378BibTeX
@inproceedings{purgal2022ijcai-learning,
title = {{Learning Higher-Order Logic Programs from Failures}},
author = {Purgal, Stanislaw J. and Cerna, David M. and Kaliszyk, Cezary},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {2726-2733},
doi = {10.24963/IJCAI.2022/378},
url = {https://mlanthology.org/ijcai/2022/purgal2022ijcai-learning/}
}