Meta-interpretive learning: application to grammatical inference
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In Muggleton et al. 2014, definite clause grammars were learned with predicate invention using MIL.
“By contrast, in (Muggleton et al. 2014) definite clause grammars were learned with predicate invention using Meta-Interpretive Learning (MIL).”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton et al. 2014
A recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine.
“However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton et al. 2014
MIL supports efficient predicate invention and learning of recursive logic programs built as a set of metalogical substitutions by a modified Prolog meta-interpreter.
“MIL (Muggleton and Lin 2013; Muggleton et al. 2014; Lin et al. 2014) is a technique which supports efficient predicate invention and learning of recursive logic programs built as a set of metalogical substitutions by a modified Prolog meta-interpreter (see Fig. 2) which acts as the central part of the ILP learning engine.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton and Lin 2013
MIL supports efficient predicate invention and learning of recursive logic programs built as a set of metalogical substitutions by a modified Prolog meta-interpreter.
“MIL (Muggleton and Lin 2013; Muggleton et al. 2014; Lin et al. 2014) is a technique which supports efficient predicate invention and learning of recursive logic programs built as a set of metalogical substitutions by a modified Prolog meta-interpreter (see Fig. 2) which acts as the central part of the ILP learning engine.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton et al. 2014
MIL implementations have been demonstrated to search the hypothesis space 100-1,000 times faster than state-of-the-art ILP systems in the task of learning recursive grammars.
“Owing to the efficiency of Prolog backtracking MIL implementations have been demonstrated to search the hypothesis space 100-1,000 times faster than state-of-the-art ILP systems (Muggleton et al. 2014) in the task of learning recursive grammars.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton et al. 2014
The approach can also be implemented within more sophisticated solvers such as ASP.
“However, the approach can also be implemented within more sophisticated solvers such as ASP (see Muggleton et al. 2014).”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton et al. 2014
Much recent work in ILP uses ASP to learn Datalog, definite, normal, and answer set programs.
“Much recent work in ILP uses ASP to learn Datalog (Evans et al. 2019), definite (Muggleton et al. 2014; Kaminski et al. 2018; Cropper and Dumancic 2020), normal (Ray 2009; Corapi et al. 2011; Athakravi et al. 2013), and answer set programs (Law et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Muggleton et al. 2014
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Cited by
Sorted by citations
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
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Self-citationLearning programs by learning from failures
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“Much recent work in ILP uses ASP to learn Datalog (Evans et al. 2019), definite (Muggleton et al. 2014; Kaminski et al. 2018; Cropper and Dumancic 2020), normal (Ray 2009; Corapi et al. 2011; Athakravi et al. 2013), and answer set programs (Law et al. 2014).”
BackgroundMethodInductive Logic Programming At 30: A New Introduction
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“The typical linguistic tasks ILP has been applied to concern learning grammars (Muggleton et al., 2014; Law et al., 2019b; Mooney & Califf, 1995) and parsers (Zelle & Mooney, 1996, 1995; Mooney, 1999) from examples.”
BackgroundMethodInfluentialIncremental learning of event definitions with Inductive Logic Programming
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Meta-Interpretive Learning of Data Transformation Programs
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Self-citationLogical reduction of metarules
Cropper, A., & Tourret, S. (2020) · Machine Learning · doi:10.1007/s10994-019-05834-x · Cited by 29 · see citing works
Learning higher-order logic programs
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Self-citation
- Authors
- Muggleton, S. H., Lin, D., Pahlavi, N., & Tamaddoni-Nezhad, A.
- Affiliations
- Imperial College London (GB)
- Corresponding author
- Muggleton, Stephen H.
- Venue
- Machine Learning
- Published
- 2013-05-01
- Volume
- 94
- Issue
- 1
- Pages
- 25-49
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2091092523
- Semantic Scholar
- 1801aa99bfe09a3ac2f74a2208047334cc8aef59
- Cited by
- 120
- References
- 45
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 4 · 2023: 8 · 2022: 13 · 2021: 11 · 2020: 5 · 2019: 11 · 2018: 18 · 2017: 11 · 2016: 10 · 2015: 11 · 2014: 5 · 2013: 3
- FWCI
- 10.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (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
- predicate invention, grammatical inference, regular grammars, context-free grammars, abduction, meta-interpreter, answer set programming, Prolog, prior knowledge
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