Learning Higher-Order Logic Programs From Failures

Description

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.

Period	25 Jul 2022
Event title	IJCAI International Joint Conferences on Artificial Intelligence Organization
Event type	Conference
Location	AustriaShow on map