Skip to main content

A Perspective on Inductive Logic Programming

  • Chapter

Part of the book series: Artificial Intelligence ((AI))

Summary

The state-of-the-art in inductive logic programming is surveyed by analyzing the approach taken by this field over the past 8 years. The analysis investigates the roles of 1) logic programming and machine learning, 2) theory, techniques and applications, and 3) various technical problems addressed within inductive logic programming.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Muggleton, S. : Inductive logic programming. New Generation Computing, Vol. 8:4, pp. 295–318, 1991.

    Article  Google Scholar 

  2. Muggleton, S. (Ed.): Proceedings of the lsst International Workshop on Inductive Logic Programming, Viano de Castelo, Portugal, 1991.

    Google Scholar 

  3. Muggleton, S. (Ed.): Proceedings of the 2nd International Workshop on Inductive Logic Programming, ICOT-Report, Japan, 1992.

    Google Scholar 

  4. Muggleton, S. (Ed.): Proceedings of the 3rd International Workshop on Inductive Logic Programming, Ljubjana, JSI-Report, 1993.

    Google Scholar 

  5. Wrobel, S. (Ed.): Proceedings of the 4th International Workshop on Inductive Logic Programming, Bad Honnef, Germany, GMD-Report, 1994.

    Google Scholar 

  6. De Raedt, L. (Ed.): Proceedings of the 5th International Workshop on Inductive Logic Programming, Leuven, Belgium, KUL-Report, 1995.

    Google Scholar 

  7. Muggleton, S. (Ed.): Proceedings of the 6th International Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, Vol. 1314, Springer-Verlag, 1997.

    Google Scholar 

  8. Lavrac, N., Dzeroski, S. (Eds): Proceedings of the 7th International Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, Vol. 1297, Springer-Verlag, 1997.

    Google Scholar 

  9. Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods, Journal of Logic Programming, Vol. 19–20, 1994.

    Article  Google Scholar 

  10. De Raedt, L. (Ed.):Advances in inductive logic programming. IOS Press, 1996.

    MATH  Google Scholar 

  11. Nienhuys-Cheng, SH, de Wolf, R.: Foundations of inductive logic programming, Lecture Notes in Artificial Intelligence, Vol. 1228, Springer-Verlag, 1997.

    Google Scholar 

  12. Mitchell, T. Machine Learning, Mc Graw-Hill, 1997.

    Google Scholar 

  13. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (Eds.): Advances in Knowledge Discovery and Data Mining. MIT Press, 1996.

    Google Scholar 

  14. Srinivasan, A., Muggleton, S.H., Sternberg, M.J.E., King, R.D.: Theories for mutagenicity: a study in first-order and feature-based induction. Artificial Intelligence, Vol. 85, pp. 277–299, 1995.

    Article  Google Scholar 

  15. Jaffar, J., Maher, M.: Constraint logic programming: a survey. Journal of Logic Programming, Vol. 19–20, 1994.

    Google Scholar 

  16. Muggleton S. : Inverse entailment and Progol. New Generation Computing, Vol. 13:3/4, pp. 245–286, 1995.

    Article  Google Scholar 

  17. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning, Vol. 5, pp. 239–266, 1990.

    Google Scholar 

  18. Emde, W., Wettschereck, D.: Relational instance based learning, in Saitta, L. (Ed.) Proceedings of the 13th International Conference on Machine Learning, Morgan Kaufmann, 1996.

    Google Scholar 

  19. De Raedt, L., Dehaspe, L. Clausal Discovery. Machine Learning, Vol. 26, pp. 99–146, 1997.

    Article  MATH  Google Scholar 

  20. Wrobel, S. An algorithm for multi-relational discovery of subgroups. In Ko- morowski, J., Zytkow, J. (Eds.), Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, Springer-Verlag, 1997.

    Google Scholar 

  21. Blockeel, H., De Raedt, L.: Top-down induction of logical decision trees. Artificial Intelligence, Vol. 101:1/2, pp. 285–2971998.

    Article  Google Scholar 

  22. Bratko, I., Muggleton, S.: Applications of inductive logic programming, Communications of the ACM, Vol. 38, pp. 65–70, 1995.

    Article  Google Scholar 

  23. Kearns, M., Vazzirani, U.: An introduction to computational learning theory. MIT Press, 1994.

    Google Scholar 

  24. Cohen, W., Page, CD: Polynomial Learnability and Inductive Logic Programming: Methods and Results. New Generation Computing, Vol. 13:3/4, pp. 369–409, 1995.

    Article  Google Scholar 

  25. Khaxdon, R. Learning first order universal horn expressions. In Proceedings of the 11th International Conference on Computational Learning Theory, Morgan Kaufmann, 1998.

    Google Scholar 

  26. Chandra, R., Tadepalli, P. Learning horn definitions using equivalence and membership queries. In Lavrac, N., Dzeroski, S. (Eds.) Proceedings of the 7th International Workshop on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, Vol. 1297, Springer-Verlag, 1997.

    Google Scholar 

  27. Shapiro, EY : Algorithmic Program Debugging. The MIT Press, 1982.

    Google Scholar 

  28. Flach, P., Kakas, T. (Eds.): Proceedings of the workshops on Abudction and Induction, 1996, 1997, 1998.

    Google Scholar 

  29. Dolsak, B., Muggleton, S.: The application of inductive logic programming to finite-element mesh design. In Muggleton, S. (Ed.) Inductive Logic Programming, Academic Press, 1992.

    Google Scholar 

  30. Kirsten, M., Wrobel, S. : Relational distance-based clustering. In Page, D. (Ed.) Proceedings of the 8th International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, Vol. 1446, Springer-Verlag, 1998.

    Google Scholar 

  31. Dzeroski, S., De Raedt, L., Blockeel, H.: Relational reinforcement learning, In Page, D. (Ed.) Proceedings of the 8th International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence, Vol. 1446, Springer-Verlag, 1998.

    Google Scholar 

  32. Imielinski, T., Mannila, H.,: A database perspective on knowledge discovery. Communications of the ACM, Vol. 39, pp. 58–64, 1996.

    Article  Google Scholar 

  33. De Raedt, L.: A relational database mining query language. In Plaza, J., Calmet, J. (Eds.) In Artificial Intelligence and Symbolic Computation. Lecture Notes in Artificial Intelligence, Vol. 1476, Invited Paper, Springer-Verlag, 1998.

    Google Scholar 

  34. Muggleton, S., Page, D. A learnability for universal representations, in Wrobel, S. (Ed.) Proceedings of the 4th International Workshop on Inductive Logic Programming, 1994.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

De Raedt, L. (1999). A Perspective on Inductive Logic Programming. In: Apt, K.R., Marek, V.W., Truszczynski, M., Warren, D.S. (eds) The Logic Programming Paradigm. Artificial Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60085-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-60085-2_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64249-4

  • Online ISBN: 978-3-642-60085-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics