Learning Higher-Order Logic Programs From Failures

0559054 - ÚI 2023 RIV AT eng C - Konferenční příspěvek (zahraniční konf.)
Purgal, S. J. - Cerna, David M. - Kaliszyk, C.
Learning Higher-Order Logic Programs From Failures.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22). Vienna: International Joint Conferences on Artificial Intelligence, 2022 - (De Raedt, L.), s. 2726-2733. ISBN 978-1-956792-00-3.
[IJCAI-ECAI 2022: International Joint Conference on Artificial Intelligence and the European Conference on Artificial Intelligence /31. and 25./. Vienna (AT), 23.07.2022-29.07.2022]
Grant CEP: GA MŠMT(CZ) EF18_053/0017594
Institucionální podpora: RVO:67985807
Klíčová slova: Inductive logic programing * higher-order logic * Learning from failures
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://dx.doi.org/10.24963/ijcai.2022/378

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 the666 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.
Trvalý link: https://hdl.handle.net/11104/0332473