author = {Stanislav Purgal and David Cerna and Cezary Kalisyk},
title = {{Learning Higher-Order Programs without Meta-Interpretive Learning}},
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.},
number = {21-22},
year = {2021},
month = {December},
keywords = {Inductive Logic Programming, Higher order definitions},
length = {8},
license = {CC BY 4.0 International},
type = {RISC Report Series},
institution = {Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz},
address = {Altenberger Straße 69, 4040 Linz, Austria},
issn = {2791-4267 (online)}