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Learning Logical Definitions from Relations
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“ILP systems such as ALEPH (Srinivasan 2001) and FOIL (Quinlan 1990) have no predicate invention and limited recursion learning and therefore cannot learn recursive grammars from example sequences.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Quinlan 1990
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Indeed, many popular ILP systems, such as FOIL (Quinlan 1990), Progol (Muggleton 1995), TILDE (Blockeel and De Raedt 1998), and Aleph (Srinivasan 2001) struggle to learn recursive programs.”
From Learning programs by learning from failures (2021) · cites this paper as Quinlan 1990
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although early work in ILP explored this form of learning (Sammut, 1981; Quinlan, 1990), it has been under-explored until recently (Lin et al., 2014; Cropper, 2019, 2020; Hocquette & Muggleton, 2020; Dumancic et al., 2021), mostly because of new techniques for PI.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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“Quinlan's (1990) FOIL system is one of the most well-known ILP systems and is a natural extension of ID3 (Quinlan, 1986) from the propositional setting to the first-order setting.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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“The above paragraph refers only to generality orders on single clauses, as many systems employ the covering algorithm whereby hypotheses are constructed iteratively clause by clause (Quinlan, 1990; De Raedt & Dehaspe, 1997; Muggleton, 1995; Blockeel & De Raedt, 1998; Srinivasan, 2001).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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“Top-down algorithms, (Quinlan, 1990; Blockeel & De Raedt, 1998; Bratko, 1999; Muggleton et al., 2008) start with a general hypothesis and then specialise it.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
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Metadata
- Authors
- Quinlan, J. R.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Quinlan, J.R.
- Venue
- Machine Learning
- Published
- 1990-08
- Volume
- 5
- Issue
- 3
- Pages
- 239-266
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1999138184
- Cited by
- 1,644
- References
- 45
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 21 · 2023: 23 · 2022: 22 · 2021: 34 · 2020: 34 · 2019: 26 · 2018: 36 · 2017: 29 · 2016: 25 · 2015: 29 · 2014: 44 · 2013: 44 · 2012: 41
- FWCI
- 47.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Australian Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- foil, inductive logic programming, Horn clauses, relational data, first-order logic, rule induction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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