Machine Invention of First-order Predicates by Inverting Resolution
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Early approaches to predicate invention based on W operators within the inverting resolution framework had inherent problems in controlling search and their completeness was never demonstrated, partly because of the lack of a declarative bias to delimit the hypothesis space.
“Early approaches were based on the use of W operators within the inverting resolution framework (Muggleton and Buntine 1988; Rouveirol and Puget 1989). However, apart from the inherent problems in controlling the search, the completeness of these approaches was never demonstrated, partly because of the lack of a declarative bias to delimit the hypothesis space.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton and Buntine 1988
Inverse resolution was one of the earliest approaches at predicate invention.
“Other notable contributions include inverse resolution (Muggleton & Buntine, 1988), which was also one of the earliest approaches at predicate invention.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Muggleton & Buntine, 1988
Bottom-up algorithms start with the examples and generalise them.
“Bottom-up algorithms start with the examples and generalise them (Muggleton, 1987; Muggleton & Buntine, 1988; Muggleton & Feng, 1990; Muggleton et al., 2009; Inoue et al., 2014).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Muggleton & Buntine, 1988
MIL supports predicate invention.
“In addition, MIL supports predicate invention , which has been repeatedly stated as an important challenge in ILP (Muggleton and Buntine 1988; Stahl 1995; Muggleton et al. 2012).”
From Learning higher-order logic programs (2020) · cites this paper as Muggleton and Buntine 1988
Predicate invention has been repeatedly stated as an important challenge in ILP.
“Predicate invention has been repeatedly stated as an important challenge in ILP (Muggleton and Buntine 1988; Stahl 1995; Muggleton et al. 2012).”
From Learning higher-order logic programs (2020) · cites this paper as Muggleton and Buntine 1988
Cites
Explanation-Based Generalization as Resolution Theorem Proving
KEDAR-CABELLI, S. T., & MCCARTY, L. T. (1987) · Proceedings of the Fourth International Workshop on MACHINE LEARNING · doi:10.1016/b978-0-934613-41-5.50041-6 · Cited by 123 · see citing works
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Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644 · see citing works
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
Self-citationMeta-interpretive learning: application to grammatical inference
Muggleton, S. H., Lin, D., Pahlavi, N., & Tamaddoni-Nezhad, A. (2014) · Machine Learning · doi:10.1007/s10994-013-5358-3 · Cited by 120 · see citing works
Self-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
“Predicate invention has repeatedly been stated as an important challenge in ILP (Muggleton & Buntine, 1988; Stahl, 1995; Muggleton et al., 2012) and Russell (2019) argues that the automatic invention of new high-level concepts is the most important step needed to reach human-level AI.”
BackgroundMethodInfluentialLearning 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
“Predicate invention has been repeatedly stated as an important challenge in ILP [32,42, 33].”
BackgroundSelf-citation
- Authors
- MUGGLETON, S., & BUNTINE, W.
- Affiliations
- Turing Institute (GB)
- University of Technology Sydney (AU)
- Corresponding author
- MUGGLETON, STEPHEN
- BUNTINE, WRAY
- Venue
- Machine Learning Proceedings 1988
- Publisher
- Elsevier
- Published
- 1988
- Pages
- 339-352
- OpenAlex
- W2484153108
- Cited by
- 436
- References
- 1
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 1 · 2023: 4 · 2022: 6 · 2021: 7 · 2020: 9 · 2019: 4 · 2018: 7 · 2017: 6 · 2016: 4 · 2015: 9 · 2014: 6 · 2013: 14 · 2012: 9
- FWCI
- 21.12
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Elsevier eBooks (publishedVersion)
- Funders
- Government of the United Kingdom
- 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 › Information Systems › Data Mining Algorithms and Applications
- Keywords
- predicate invention, prior knowledge, teacher questions
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