  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.
