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The Appropriateness of Predicate Invention as Bias Shift Operation in ILP

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Stahl, Irene·1995·Machine Learning
DOI 10.1007/bf00993476
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  • MIL supports predicate invention.

    “In addition, MIL supports predicate invention , which has been repeatedly stated as an important challenge in ILP (Muggleton and Buntine 1988; Stahl 1995; Muggleton et al. 2012).”

    From Learning higher-order logic programs (2020) · cites this paper as Stahl 1995

  • Predicate invention has been repeatedly stated as an important challenge in ILP.

    “Predicate invention has been repeatedly stated as an important challenge in ILP (Muggleton and Buntine 1988; Stahl 1995; Muggleton et al. 2012).”

    From Learning higher-order logic programs (2020) · cites this paper as Stahl 1995

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Research works printed but not linked: 27

  1. In contrast to our setting, (Muggleton & DeRaedt, 1994) assume that Ee contains negative evidence, in our case negated ground atoms. This requires to rewrite the satisfiability conditions to B U EQ ty= D and B U H U Ee ^ D, respectively. References Cohen, W.. (1993). PAC-learning a restricted class of recursive logic programs. In Proc. of the 3rd Interna- tional Workshop on Inductive Logic Programming.
  2. De Raedt, L. (1992). Interactive theory revision: an inductive logic programming approach. Academic Press.
  3. De Raedt, L. & Bruynooghe, M. (1989). Towards friendly concept learners. In Proc. of IJCAI.
  4. De Raedt, L. & Bruynooghe, M. (1992). Interactive concept-learning and constructive induction by analogy. Machine Learning, 8(2): 107-150.
  5. De Raedt, L., Feyaerts, J. & Bruynooghe, M. (1991). Acquiring object-knowledge for learning systems. In Y. Kodratoff, editor, Proc. of the Fifth European Working Session on Learning. Springer.
  6. Dzeroski, S., Muggleton, S. & Russel, S. (1992). PAC-learnability of determinate logic programs. In Proc. of the 5th ACM Workshop on Computational Learning Theory.
  7. Flach, P.A. (1993). Predicate invention in inductive data engineering. In Machine Learning: ECML-93, European Conference on Machine Learning, Wien, Austria. Springer.
  8. Kietz, J.U. & Wrobel, S. (1992). Controlling the complexity of learning in logic through syntactic and task-oriented models. In S. Muggleton, editor, Inductive Logic Programming. Academic Press.
  9. Lapointe, S., Ling, C. & Matwin, S. (1993). Constructive inductive logic programming. In Proc. of the IJCA1-93.
  10. Morgan Kaufmann.
  11. Mitchell, T.M. (1980). The need for biases in learning generalizations. In J. W. Shavlik and T. G. Dietterich, editors, Readings in Machine Learning. Morgan Kaufmann.
  12. Muggleton, S. (1988). A strategy for constructing new predicates in first order logic. In Proceedings of the Third European Working Session on Learning. Pitman.
  13. Muggleton, S. (1990). Inductive logic programming. In First Conference on Algorithmic Learning Theory, Tokio, Ohmsha.
  14. Muggleton, S. (1993). Inductive logic programming: Derivations, successes and shortcoming. In Machine Learning: ECML-93, European Conference on Machine Learning, Wien, Austria. Springer.
  15. Muggleton, S. & Buntine, W. (1988). Machine invention of first-order predicates by inverting resolution. In Fifth International Conference on Machine Learning.
  16. Morgan Kaufmann.
  17. Muggleton, S. & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, Special Issue on 10 Years of Logic Programming.
  18. Muggleton, S. & Feng, C. (1990). Efficient induction of logic programs. In First Conference on Algorithmic Learning Theory, Tokyo, Ohmsha.
  19. Rouveirol, C. (1991). ITOU: Induction de Theories en Ordre Un. PhD thesis, Universite Paris Sud, Centre d'Orsay.
  20. Rouveirol, C. (1992). ITOU: Induction of first order theories. In Muggleton, S., editor, Inductive Logic Programming. Academic Press.
  21. Srinivasan, A., Muggleton, S., & Bain, M. (1992). Distinguishing exceptions from noise in non-monotonic learning. In Proceedings of ILP'92, Tokyo.
  22. Stahl, I., Tausend, B., & Wirth, R. (1993). Two methods for improving inductive logic programming systems. In Machine Learning: ECML-93, European Conference on Machine Learning, Wien, Austria. Springer.
  23. Tausend, B. (1992). Using and adapting schemes for the induction of horn clauses. In ECAI Workshop Logical Approaches to Machine Learning, Wien.
  24. Wirth. R. (1989). Lernverfahren zur Vervollstandigung von Hornklauselmengen durch inverse Resolution. Dissertation, Fakultat Informatik, Universitat Stuttgart.
  25. Wirth, R. & O'Rorke, P. (1991). Constraints on predicate invention. In Eighth International Conference on Machine Learning.
  26. Morgan Kaufmann.
  27. Yardeni, E. & Shapiro, E. (1991). A type system for logic programs. Journal of Logic Programming, (10):125-153. Received November 18, 1993 Final Manuscript August 22, 1994

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Authors
Stahl, I.
Affiliations
University of Stuttgart (DE)
Corresponding author
Stahl, Irene
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
1995
Volume
20
Issue
1-2
Pages
95-117
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1007/bf00993476
OpenAlex
W1992578025
Cited by
32
References
1
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 1 · 2013: 1
FWCI
1.43
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
Machine Learning (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
predicate invention, inductive logic programming, language bias, decidability, learning failure

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