Learning Logical Definitions from Relations
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ILP systems such as ALEPH and FOIL have no predicate invention and limited recursion learning and therefore cannot learn recursive grammars from example sequences.
“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
The Discussion (page not available in our copy) says FOIL cannot invent new predicates, and §5.7 says its handling of recursion is incomplete. But the paper also says FOIL can find recursive definitions; it does not establish that FOIL cannot learn recursive grammars from example sequences. It does not address ALEPH. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMany popular ILP systems, such as FOIL, Progol, TILDE, and Aleph struggle to learn recursive programs.
“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
The paper’s section on recursive definitions describes restrictions on the recursive clauses FOIL can learn, and §5.7 says its methods for avoiding infinite recursion are incomplete. But §5.7 also says FOIL can discover many common recursive definitions, so the broad claim that FOIL struggles to learn recursive programs overstates the limitation. The other systems have separate citations and are not assessed against this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredEarly work in ILP explored lifelong/multi-task/transfer learning, but it has been under-explored until recently, mostly because of new techniques for PI.
“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
Section 4 describes FOIL learning definitions for one target relation at a time, and Section 7 says it uses other relations as background knowledge. That supports early work on multiple relational learning tasks, but the cited passages do not show FOIL retaining or transferring what it learned between tasks. The later claim about why the area was under-explored is attached to other citations, not Quinlan (1990). (LLM, conf=0.78)
Experimental — accuracy not yet measuredQuinlan's (1990) FOIL system is one of the most well-known ILP systems and is a natural extension of ID3 from the propositional setting to the first-order setting.
“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
Many systems employ the covering algorithm whereby hypotheses are constructed iteratively clause by clause.
“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
Top-down algorithms start with a general hypothesis and then specialise it.
“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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Search-Intensive Concept Induction
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Temporal classification : extending the classification paradigm to multivariate time series
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Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
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Machine Learning and Software Engineering
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Dynamically discovering likely program invariants
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Induction of logic programs: FOIL and related systems
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Self-citationEffective learning in dynamic environments by explicit context tracking
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Trading Accuracy for Simplicity in Decision Trees
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Learning First-Order Horn Clauses from Web Text
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Bottom-up learning of Markov logic network structure
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Learning nonrecursive definitions of relations with linus
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Applications of inductive logic programming
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Wrapper Maintenance: A Machine Learning Approach
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Kernels and Distances for Structured Data
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- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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