Meta-Interpretive Learning of Data Transformation Programs
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Recent meta-level ILP approaches often learn optimal solutions, such as programs with the fewest clauses or literals.
“Recent meta-level ILP approaches often learn optimal solutions, such as programs with the fewest clauses (Muggleton et al. 2015; Cropper and Muggleton 2016; Kaminski et al. 2018) or literals (Corapi et al. 2011; Law et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Cropper and Muggleton 2016
Metagol uses a Prolog meta-interpreter to search for a proof of a meta-level Prolog program.
“For instance, Metagol (Muggleton et al., 2015; Cropper & Muggleton, 2016) uses a Prolog meta-interpreter to search for a proof of a meta-level Prolog program.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Cropper & Muggleton, 2016
Newer systems address the limitation of not guaranteeing smallest programs through meta-level reasoning.
“Newer systems address this limitation (Corapi et al., 2011; Law et al., 2014; Cropper & Muggleton, 2016; Kaminski et al., 2018; Cropper & Morel, 2021) through meta-level reasoning (Section 4.5).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Cropper & Muggleton, 2016
Metagol uses a Prolog meta-interpreter to construct a proof of a set of examples and extracts a program from the proof.
“Finally, Metagol uses a Prolog meta-interpreter to construct a proof of a set of examples and extracts a program from the proof.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Cropper & Muggleton, 2016
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- Authors
- Cropper, A., Tamaddoni-Nezhad, A., & Muggleton, S. H.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Cropper, Andrew
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 46-59
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2477228960
- Cited by
- 32
- References
- 22
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 2 · 2021: 8 · 2020: 5 · 2019: 2 · 2018: 7 · 2017: 1 · 2016: 3
- FWCI
- 3.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
- Keywords
- predicate invention, problem decomposition, semi-structured data, unstructured data, natural language
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