Abstract
AbstractInductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Reference118 articles.
1. Ahlgren, J., & Yuen, S. Y. (2013). Efficient program synthesis using constraint satisfaction in inductive logic programming. The Journal of Machine Learning Research, 14(1), 3649–3682.
2. Ai, L., Muggleton, S., Hocquette, C., Gromowski, M., & Schmid, U. (2020). Beneficial and harmful explanatory machine learning. Machine Learning, 110, 695–721.
3. Albarghouthi, A., Koutris, P., Naik, M., & Smith, C. (2017). Constraint-based synthesis of datalog programs. In 23rd international conference on principles and practice of constraint programming, CP 2017. Lecture notes in computer science (Vol. 10416, pp. 689–706). Springer.
4. Antanas, L., Moreno, P., & De Raedt, L. (2015). Relational kernel-based grasping with numerical features. In 25th international conference on inductive logic programming, ILP 2015. Lecture notes in computer science (Vol. 9575, pp. 1–14). Springer.
5. Bain, M., & Muggleton, S. (1992). Non-monotonic learning. In Inductive logic programming (pp. 145–161). Academic Press.
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