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Cited by 1,537Logic, Reasoning, and KnowledgeData Mining Algorithms and ApplicationsComputability, Logic, AI Algorithms

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Inductive Logic Programming: Theory and methods

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Muggleton, Stephen, de Raedt, Luc·1994·The Journal of Logic Programming
DOI 10.1016/0743-1066(94)90035-3
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  • Learning efficient logic programs has long been considered a difficult problem, mainly because there is no declarative difference between an efficient program, such as mergesort, and an inefficient program, such as bubble sort.

    “Learning efficient logic programs has long been considered a difficult problem (Muggleton & De Raedt, 1994; Muggleton et al., 2012), mainly because there is no declarative difference between an efficient program, such as mergesort, and an inefficient program, such as bubble sort.”

    From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Muggleton & De Raedt, 1994

    Section 11.2.1 (p. 668) says that inducing a correct sorting program does not guarantee an efficient one, supporting the difficulty of learning efficient logic programs. It does not say that this difficulty is mainly due to the absence of a declarative difference between efficient and inefficient programs, or use mergesort and bubble sort as examples. (LLM, conf=0.82)

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Research works printed but not linked: 70

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Metadata

Authors
Muggleton, S., & de Raedt, L.
Affiliations
University of Oxford (GB)
Corresponding author
Muggleton, Stephen
ORCID
Muggleton, Stephen
Venue
The Journal of Logic Programming
Publisher
Elsevier BV
Published
1994-05
Volume
19-20
Pages
629-679
ISSN
0743-1066 (ISSN-L)
DOI
10.1016/0743-1066(94)90035-3
OpenAlex
W1987902506
Cited by
1,537
References
4
Citations by year
2026: 17 · 2025: 28 · 2024: 31 · 2023: 36 · 2022: 32 · 2021: 67 · 2020: 62 · 2019: 59 · 2018: 44 · 2017: 58 · 2016: 36 · 2015: 39 · 2014: 48 · 2013: 50 · 2012: 54
FWCI
45.30
Type
journal article
Language
en
Open access
Licence
Locations
  • The Journal of Logic Programming (publishedVersion)
  • Lirias (submittedVersion, other-oa)
Funders
  • University of Oxford
  • Fonds De La Recherche Scientifique - FNRS
  • KU Leuven
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 › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
Keywords
inductive logic programming, inductive reasoning, model theory, proof theory, Bayesian confirmation, computational learning theory, predicate invention, scientific discovery, knowledge acquisition

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