Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A Refinement Operator for Theories

  • Conference paper
  • First Online:
Inductive Logic Programming (ILP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2157))

Included in the following conference series:

Abstract

Most implemented ILP systems construct hypotheses clause by clause using a refinement operator for clauses. To avoid the problems faced by such greedy covering algorithms, more flexible refinement operators for theories are needed. In this paper we construct a syntactically monotonic, finite and solution-complete refinement operator for theories, which eliminates certain annoying redundancies (due to clause deletions), while also addressing the limitations faced by HYPER’s refinement operator (which are mainly due to keeping the number of clauses constant during refinement).

We also show how to eliminate the redundancies due to the commutativity of refinement operations while preserving weak completeness as well as a limited form of flexibility. The refinement operator presented in this paper represents a first step towards constructing more efficient and flexible ILP systems with precise theoretical guarantees.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Badea Liviu, Stanciu M. Refinement Operators can be (weakly) perfect. Proceedings ILP-99, pp. 21–32, LNAI 1634, Springer Verlag, 1999.

    Google Scholar 

  2. Badea Liviu. Perfect Refinement Operators can be Flexible. In Werner Horn, editor, Proceedings ECAI-2000, pp. 266–270. IOS Press, August 2000.

    Google Scholar 

  3. Bratko I. Refining Complete Hypotheses in ILP. Proceedings ILP-99, pp. 44–55, LNAI 1634, Springer Verlag, 1999.

    Google Scholar 

  4. Nienhuys-Cheng S.H., de Wolf R. Foundations of Inductive Logic Programming. LNAI 1228, Springer Verlag 1997.

    Google Scholar 

  5. De Raedt L., Lavrac N., Dzeroski S. Multiple Predicate Learning. In R. Bajcsy, editor, Proceedings IJCAI-93, pages 1037–1043. Morgan Kaufmann, 1993.

    Google Scholar 

  6. Midelfart H. A Bounded Search Space of Clausal Theories. Proc. ILP-99, 210–221.

    Google Scholar 

  7. Muggleton S. Inverse entailment and Progol. New Generation Computing Journal, 13:245–286, 1995.

    Article  Google Scholar 

  8. Quinlan J.R. Learning Logical Definitions from Relations. Machine Learning 5:239–266, 1990.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AI Lab, National Institute for Research and Development in Informatics, 8-10 Averescu Blvd., Bucharest, Romania

    Liviu Badea

Authors
  1. Liviu Badea
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Université Paris Sud, LRI, bât. 490, 91405, Orsay, France

    Céline Rouveirol

  2. Ecole Polytechnique, LMS, 91128, Palaiseau, France

    Michéle Sebag

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Badea, L. (2001). A Refinement Operator for Theories. In: Rouveirol, C., Sebag, M. (eds) Inductive Logic Programming. ILP 2001. Lecture Notes in Computer Science(), vol 2157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44797-0_1

Download citation

  • DOI: https://doi.org/10.1007/3-540-44797-0_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42538-0

  • Online ISBN: 978-3-540-44797-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation