Learning Higher-Order Programs without Meta-Interpretive Learning
- URL: http://arxiv.org/abs/2112.14603v1
- Date: Wed, 29 Dec 2021 15:27:27 GMT
- Title: Learning Higher-Order Programs without Meta-Interpretive Learning
- Authors: Stanis{\l}aw J. Purga{\l}, David M. Cerna, Cezary Kaliszyk
- Abstract summary: 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.
- Score: 2.0518509649405106
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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. Furthermore, we provide a theoretical
framework capturing the class of higher-order definitions handled by our
extension.
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