Typed Meta-interpretive Learning of Logic Programs
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More complex types, such as polymorphic types, can achieve better pruning for programs over structured data.
“However, more complex types, such as polymorphic types, can achieve better pruning for programs over structured data (Morel et al. 2019).”
From Learning programs by learning from failures (2021) · cites this paper as Morel et al. 2019
Our empirical results contradict those by Kaminski et al. (2018), but support those by Morel et al. (2019).
“Our empirical results contradict those by Kaminski et al. (2018), but support those by Morel et al. (2019).”
From Learning higher-order logic programs (2020) · cites this paper as Morel et al. (2019)
Cites
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Muggleton, S. H., Lin, D., & Tamaddoni-Nezhad, A. (2015) · Machine Learning · doi:10.1007/s10994-014-5471-y · Cited by 184 · see citing works
Inductive Learning of Answer Set Programs
Law, M., Russo, A., & Broda, K. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-11558-0_22 · Cited by 133 · see citing works
Structure Learning via Parameter Learning
Wang, W. Y., Mazaitis, K., & Cohen, W. W. (2014) · Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management · doi:10.1145/2661829.2662022 · Cited by 47 · see citing works
Constraint-Based Synthesis of Datalog Programs
Albarghouthi, A., Koutris, P., Naik, M., & Smith, C. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66158-2_44 · Cited by 40 · see citing works
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Learning programs by learning from failures
Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · see citing works
“However, more complex types, such as polymorphic types, can achieve better pruning for programs over structured data (Morel et al. 2019).”
BackgroundSelf-citationInductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
“This succinctness makes Metagol extremely easy to adapt, such as to add negation (Siebers & Schmid, 2018), to add types (Morel et al., 2019), or to learn efficient programs (Cropper & Muggleton, 2015, 2019).”
BackgroundSelf-citationLogical reduction of metarules
Cropper, A., & Tourret, S. (2020) · Machine Learning · doi:10.1007/s10994-019-05834-x · Cited by 29 · see citing works
Self-citationLearning higher-order logic programs
Cropper, A., Morel, R., & Muggleton, S. (2020) · Machine Learning · doi:10.1007/s10994-019-05862-7 · Cited by 11 · see citing works
“erforms HEXMIL, and Learning higher-order logic programs 31 similarly Metagolho outperforms HEXMILho. Our empirical results contradict those by Kaminski et al. [23], but support those by Morel et al. [30]. There are multiple explanations for this discrepancy. We think that the main problem is the ASP grounding problem…”
ResultSelf-citation
- Authors
- Morel, R., Cropper, A., & Ong, C.-H. L.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Morel, Rolf
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 198-213
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2943979673
- Cited by
- 11
- References
- 4
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 4 · 2019: 2
- FWCI
- 0.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
- Keywords
- inductive logic programming, type systems, type checking, polymorphic types, answer set programming, Prolog
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