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Cited by 21Logic, Reasoning, and KnowledgeData Mining Algorithms and ApplicationsAI-based Problem Solving and Planning

Best-effort inductive logic programming via fine-grained cost-based hypothesis generation: The inspire system at the inductive logic programming competition

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Schüller, Peter, Benz, Mishal·2018·Machine Learning
DOI 10.1007/s10994-018-5708-2
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  • ASP-encoded ILP systems often struggle with scalability, especially in terms of the problem domain size.

    “Systems that implement this approach can often learn optimal and recursive programs and can harness state-of-the-art ASP solvers, but often struggle with scalability, especially in terms of the problem domain size.”

    From Learning programs by learning from failures (2021) · cites this paper as Schüller and Benz 2018

    The Related work section says ASP-based ILP introduced scalability limitations. It does not identify problem-domain size as a particular cause or dimension of those limitations; the Conclusion instead discusses scaling to large amounts of training examples. No page numbers are available in the supplied passages. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • ASP-encoded ILP systems can often learn optimal and recursive programs.

    “Systems that implement this approach can often learn optimal and recursive programs and can harness state-of-the-art ASP solvers, but often struggle with scalability, especially in terms of the problem domain size.”

    From Learning programs by learning from failures (2021) · cites this paper as Schüller and Benz 2018

    Section 4 describes searching for an optimal hypothesis, and Section 3.3 discusses configuring the hypothesis space to include a transitive-closure rule. These passages support optimal search and the possibility of recursive rules, but they do not establish that ASP-based ILP systems often learn recursive programs. The paper discusses scalability limitations in Section 7, without specifically identifying problem-domain size as the main limitation. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • INSPIRE also uses predefined constraints to remove redundancy from the hypothesis space.

    “INSPIRE (Schüller and Benz 2018) also uses predefined constraints to remove redundancy from the hypothesis space (in INSPIRE's case, each hypothesis is a single clause).”

    From Learning programs by learning from failures (2021) · cites this paper as Schüller and Benz 2018

    Section 3.6 (Redundancy elimination module; page unavailable) describes constraints that eliminate redundant rules from INSPIRE’s hypothesis search space. But the parenthetical is not supported: Section 3 (Declarative hypothesis generation; page unavailable) says each generated answer set represents a single rule that may be part of a hypothesis, not that each hypothesis is a single clause. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 31

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Cited by

Sorted by citations

  • Learning programs by learning from failures

    Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · see citing works

    “The idea is to frame the problem as an ASP problem where an answer set (a model) corresponds to a program, an approach also employed by other recent ILP approaches (Corapi et al. 2011; Law et al. 2014; Kaminski et al. 2018; Schüller and Benz 2018).”
    BackgroundMethodInfluential

Authors
Schüller, P., & Benz, M.
Affiliations
  • TU Wien (AT)
  • Karlsruhe Institute of Technology (DE)
Corresponding author
Schüller, Peter
ORCID
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
2018-05-22
Volume
107
Issue
7
Pages
1141-1169
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1007/s10994-018-5708-2
OpenAlex
W2736057823
Cited by
21
References
6
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 3 · 2021: 6 · 2020: 2 · 2019: 3 · 2018: 2
FWCI
1.83
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Machine Learning (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Dspace Repository (Marmara Üniversitesi) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • Türkiye Bilimsel ve Teknolojik Araştirma Kurumu (114E777)
  • Ôsterreichische Forschungsförderungsgesellschaft (861263)
  • Austrian Science Fund (P27730)
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 › AI-based Problem Solving and Planning
Keywords
inductive logic programming, answer set programming, hypothesis generation, rule complexity

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