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Derivation Reduction of Metarules in Meta-interpretive Learning

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Cropper, Andrew, Tourret, Sophie·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-99960-9_1
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Crossref · 10.1007/978-3-319-99960-9_1

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3 of 3 references verified0 citation errors · 0 not checkable

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  • Eight of the metarules used in our experiments are a subset of a derivationally irreducible set of monadic and dyadic metarules.

    “Eight of these metarules (the ones with only monadic or dyadic literals) are a subset of a derivationally irreducible set of monadic and dyadic metarules (Cropper and Tourret 2018).”

    From Learning higher-order logic programs (2020) · cites this paper as Cropper and Tourret 2018

    Section 4 describes reducing sets of metarules to derivationally minimal sets, and the discussion of derivation reduction defines what makes a set derivationally irreducible. The cited paper does not identify the citing authors’ eight metarules, so our copy cannot establish that those particular eight are a subset of one of its irreducible sets. Page numbers are unavailable in our copy. (LLM, conf=0.82)

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Cites

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Research works printed but not linked: 21

  1. Aaron R Bradley and Zohar Manna. The calculus of computation: decision procedures with applications to verification. Springer Science & Business Media, 2007.
  2. Andrew Cropper and Stephen H. Muggleton. Logical minimisation of meta-rules within meta-interpretive learning. In Jesse Davis and Jan Ramon, editors, Inductive Logic Programming -24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers, volume 9046 of Lecture Notes in Computer Science, pages 62-75. Springer, 2014.
  3. Andrew Cropper and Stephen H. Muggleton. Learning higher-order logic programs through abstraction and invention. In Subbarao Kambhampati, editor, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pages 1418-1424. IJCAI/AAAI Press, 2016.
  4. Andrew Cropper and Stephen H. Muggleton. Learning efficient logic programs. Machine Learning, pages 1-21, 2018.
  5. Werner Emde, Christopher Habel, and Claus-Rainer Rollinger. The discovery of the equator or concept driven learning. In Alan Bundy, editor, Proceedings of the 8th International Joint Conference on Artificial Intelligence. Karlsruhe, FRG, August 1983, pages 455-458.
  6. Pierre Flener. Inductive logic program synthesis with DIALOGS. In Stephen Muggleton, editor, Inductive Logic Programming, 6th International Workshop, ILP-96, Stockholm, Sweden, August 26-28, 1996, Selected Papers, volume 1314 of Lecture Notes in Computer Science, pages 175-198. Springer, 1996.
  7. Nuno A. Fonseca, Vítor Santos Costa, Fernando M. A. Silva, and Rui Camacho. On avoiding redundancy in inductive logic programming. In Rui Camacho, Ross D. King, and Ashwin Srinivasan, editors, Inductive Logic Programming, 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004, Proceedings, volume 3194 of Lecture Notes in Computer Science, pages 132-146. Springer, 2004.
  8. Marijn Heule, Matti Järvisalo, Florian Lonsing, Martina Seidl, and Armin Biere. Clause elimination for sat and qsat. Journal of Artificial Intelligence Research, 53:127-168, 2015.
  9. Tobias Kaminski, Thomas Eiter, and Katsumi Inoue. Exploiting answer set programming with external sources for meta-interpretive learning. In 34th International Conference on Logic Programming, 2018.
  10. Robert A. Kowalski. Predicate logic as programming language. In IFIP Congress, pages 569-574, 1974.
  11. Marina Langlois, Dhruv Mubayi, Robert Sloan, and György Turán. Combinatorial problems for horn clauses. Graph Theory, Computational Intelligence and Thought, pages 54-65, 2009.
  12. Paolo Liberatore. Redundancy in logic I: CNF propositional formulae. Artif. Intell., 163(2):203-232, 2005.
  13. Paolo Liberatore. Redundancy in logic II: 2cnf and horn propositional formulae. Artif. Intell., 172(2-3):265-299, 2008.
  14. Dianhuan Lin, Eyal Dechter, Kevin Ellis, Joshua B. Tenenbaum, and Stephen Muggleton. Bias reformulation for one-shot function induction. In ECAI 2014 -21st European Conference on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic -Including Prestigious Applications of Intelligent Systems (PAIS 2014), pages 525-530, 2014.
  15. John W Lloyd. Foundations of logic programming. Springer Science & Business Media, 2012.
  16. Jerzy Marcinkowski and Leszek Pacholski. Undecidability of the horn-clause implication problem. In 33rd Annual Symposium on Foundations of Computer Science, Pittsburgh, Pennsylvania, USA, 24-27 October 1992, pages 354-362, 1992.
  17. Stephen Muggleton and Cao Feng. Efficient induction of logic programs. In Algorithmic Learning Theory, First International Workshop, ALT '90, Tokyo, Japan, October 8-10, 1990, Proceedings, pages 368-381, 1990.
  18. Shan-Hwei Nienhuys-Cheng and Ronald de Wolf. Foundations of Inductive Logic Programming. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1997.
  19. G.D. Plotkin. Automatic Methods of Inductive Inference. PhD thesis, Edinburgh University, August 1971.
  20. Luc De Raedt. Declarative modeling for machine learning and data mining. In Algorithmic Learning Theory -23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings, page 12, 2012.
  21. E.Y. Shapiro. Algorithmic program debugging. MIT Press, 1983.

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Authors
Cropper, A., & Tourret, S.
Affiliations
  • University of Oxford (GB)
  • Max Planck Institute for Informatics (DE)
Corresponding author
Cropper, Andrew
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
1-21
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-319-99960-9_1
OpenAlex
W2888757411
Cited by
15
References
3
Citations by year
2024: 1 · 2022: 2 · 2021: 3 · 2020: 2 · 2019: 7
FWCI
1.22
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
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
meta-rules, hypothesis space, SLD-resolution, Progol, inductive logic programming

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