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.
Keywords
- Knowledge Representation and Reasoning: Learning and reasoning
- Knowledge Representation and Reasoning: Applications
- Knowledge Representation and Reasoning: Logic Programming
