author = {Stanislav Purgal and David Cerna and Cezary Kalisyk},
title = {{Learning Higher-Order Programs without Meta-Interpretive Learning}},
booktitle = {{Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22}},
language = {english},
abstract = {Learning complex programs through textit{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 textit{Learning From Failures} paradigm by higher-order definitions significantly improves learning performance without the burdensome human guidance required by existing systems. Furthermore, we provide a theoretical framework capturing the class of higher-order definitions handled by our extension.},
series = {Main Track},
pages = {2726--2733},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
isbn_issn = {10.24963/ijcai.2022/378},
year = {2022},
month = {july},
editor = {Lud De Raedt},
refereed = {yes},
keywords = {Inductive Logic Programming, Higher order definitions},
length = {8},
conferencename = {International Joint Conference on Artificial Intelligence},
url = {https://doi.org/10.35011/risc.21-22}