Advertisement

Incremental Learning of Functional Logic Programs

  • C. Ferri-Ramírez
  • J. Hernández-Orallo
  • M.J. Ramírez-Quintana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2024)

Abstract

In this work, we consider the extension of the Inductive Functional Logic Programming (IFLP) framework in order to learn functions in an incremental way. In general, incremental learning is necessary when the number of examples is infinite, very large or presented one by one. We have performed this extension in the FLIP system, an implementation of the IFLP framework. Several examples of programs which have been induced indicate that our extension pays off in practice. An experimental study of some parameters which affect this efficiency is performed and some applications for programming practice are illustrated, especially small classification problems and data-mining of semi-structured data.

Keywords

Inductive functional logic programming (IFLP) inductive logic programming (ILP) incremental learning theory revision 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. Abiteboul, P. Buneman, and D. Suciu. Data on the web: from relations to semistructured data and XML. Morgan Kaufmann Publishers, 1999.Google Scholar
  2. 2.
    F. Bergadano and D. Gunetti. Inductive Logic Programming: from Machine Learning to Software Engineering. The MIT Press, Cambridge, Mass., 1995.Google Scholar
  3. 3.
    J. Cendrowska. Prism: An algorithm for inducing modular rules. International Journal of Man-Machines Studies, 27:349–370, 1987.zbMATHCrossRefGoogle Scholar
  4. 4.
    C. Ferri, J. Hernández, and M.J. Ramírez. The FLIP system homepage. http://www.dsic.upv.es/~jorallo/flip/, 2000.
  5. 5.
    C. Ferri, J. Hernández, and M.J. Ramírez. The FLIP user’s manual (v0.7). Technical report, Department of Information Systems and Computation, Valencia University of Technology, 2000/24, 2000.Google Scholar
  6. 6.
    R. Godin and R. Missaoui. An incremental concept formation approach for learning from databases. Theoretical Computer Science, 133:387–419, 1994.zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    M. Hanus. The Integration of Functions into Logic Programming: From Theory to Practice. Journal of Logic Programming, 19’20:583–628, 1994.CrossRefMathSciNetGoogle Scholar
  8. 8.
    J. Hernández and M.J. Ramírez. Inverse Narrowing for the Induction of Functional Logic Programs. In Proc. Joint Conference on Declarative Programming, APPIA GULP-PRODE’98, pages 379–393, 1998.Google Scholar
  9. 9.
    J. Hernández and M.J. Ramírez. A Strong Complete Schema for Inductive Functional Logic Programming. In Proc. of the Ninth International Workshop on Inductive Logic Programming, ILP’99, volume 1634 of Lecture Notes in Artificial Intelligence, pages 116–127, 1999.Google Scholar
  10. 10.
    J. Hernández and M.J. Ramírez. Predictive Software. Automated Software Engineering, to appear, 2001.Google Scholar
  11. 11.
    H. Katsuno and A. O. Mendelzon. On the difference between updating a knowledge base and revising it. In Proc of the 2nd Intern. Conf. on Princip. of Knowledge Representation and Reasoning, pages 387–394. M. Kaufmann Publishers, 1991.Google Scholar
  12. 12.
    J. U. Kietz and S. Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, 1992.Google Scholar
  13. 13.
    S. Muggleton. Inductive Logic Programming. New Generation Computing, 8(4):295–318, 1991.zbMATHCrossRefGoogle Scholar
  14. 14.
    S. Muggleton. Inductive logic programming: Issues, results, and the challenge of learning language in logic. Artificial Intelligence, 114(1-2):283–296, 1999.zbMATHCrossRefGoogle Scholar
  15. 15.
    S. Muggleton and W. Buntine. Machine invention of first-order predicates by inverting resolution. In S. Muggleton, editor, Inductive Logic Programming, pages 261–280. Academic Press, 1992.Google Scholar
  16. 16.
    University of California. UCI Machine Learning Repository Content Summary. http://www.ics.uci.edu/~mlearn/MLSummary.html.
  17. 17.
    L. De Raedt. Interactive Theory Revision: An Inductive Logic Programming Approach. Academic Press, 1992.Google Scholar
  18. 18.
    M. Krishna Rao. A framework for incremental learning of logic programs. Theoretical Computer Science, 185:191–213, 1997.zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    B. L. Richards and R. J. Mooney. First order theory revision. In Proc. of the 8th International Workshop on Machine Learning, pages 447–451. Morgan Kaufmann, 1991.Google Scholar
  20. 20.
    B. L. Richards and R. J. Mooney. Automated refinement of first-order horn-clause domain theories. Machine Learning, 19:95–131, 1995.Google Scholar
  21. 21.
    S. Wrobel. On the proper definition of minimality in spezialization and theory revision. In P.B. Brazdil, editor, Proc. of ECML-93, volume 667 of Lecture Notes in Computer Science, pages 65–82. Springer-Verlag, 1993.Google Scholar
  22. 22.
    S. Wrobel. First order theory refinement. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 14–33. IOS Press, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • C. Ferri-Ramírez
    • 1
  • J. Hernández-Orallo
    • 1
  • M.J. Ramírez-Quintana
    • 1
  1. 1.DSICUPVValenciaSpain

Personalised recommendations