Learning Higher-Order Programs without Meta-Interpretive Learning

Autor:	Purgał, Stanisław J., Cerna, David M., Kaliszyk, Cezary
Rok vydání:	2021
Předmět:	Computer Science - Artificial Intelligence Computer Science - Logic in Computer Science
Druh dokumentu:	Working Paper
DOI:	10.24963/ijcai.2022/378
Popis:	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. Comment: Accepted at IJCAI 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.14603 Zobrazit plný text záznamu View this record from Arxiv