Skip to main content
SpringerLink
Log in

A system for abductive learning of logic programs

  • Conference paper
  • First Online:
Logic Programming and Knowledge Representation (LPKR 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1471))

Abstract

We present the system LAP (Learning Abductive Programs) that is able to learn abductive logic programs from examples and from a background abductive theory. A new type of induction problem has been defined as an extension of the Inductive Logic Programming framework. In the new problem definition, both the background and the target theories are abductive logic programs and abductive derivability is used as the coverage relation.

LAP is based on the basic top-down ILP algorithm that has been suitably extended. In particular, coverage of examples is tested by using the abductive proof procedure defined by Kakas and Mancarella [24]. Assumptions can be made in order to cover positive examples and to avoid the coverage of negative ones, and these assumptions can be used as new training data. LAP can be applied for learning in the presence of incomplete knowledge and for learning exceptions to classification rules.

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.

References

  1. H. Adé and M. Denecker. RUTH: An ILP theory revision system. In Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems, 1994.

    Google Scholar 

  2. H. Adé and M. Denecker. AILP: Abductive inductive logic programming. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1995.

    Google Scholar 

  3. J. J. Alferes and L. M. Pereira. Reasoning with Logic Programming, volume 1111 of LNAI. SV, Heidelberg, 1996.

    Google Scholar 

  4. M. Bain and S. Muggleton. Non-monotonic learning. In S. Muggleton, editor, Inductive Logic Programming, chapter 7, pages 145–161. Academic Press, 1992.

    Google Scholar 

  5. M. Bain and S. Muggleton. Non-monotonic learning. In S. Muggleton, editor, Inductive Logic Programming, pages 145–161. Academic Press, 1992.

    Google Scholar 

  6. F. Bergadano and D. Gunetti. Learning Clauses by Tracing Derivations. In Proceedings 4th Int. Workshop on Inductive Logic Programming, 1994.

    Google Scholar 

  7. F. Bergadano and D. Gunetti. Inductive Logic Programming. MIT press, 1995.

    Google Scholar 

  8. F. Bergadano, D. Gunetti, M. Nicosia, and G. Ruffo. Learning logic programs with negation as failure. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 107–123. IOS Press, 1996.

    Google Scholar 

  9. A. Brogi, E. Lamma, P. Mancarella, and P. Mello. A unifying view for logic programming with non-monotonic reasoning. Theoretical Computer Science, 184:1–59, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  10. L. De Raedt and M. Bruynooghe. On negation and three-valued logic in interactive concept learning. In Proceedings of the 9th European Conference on Artificial Intelligence, 1990.

    Google Scholar 

  11. L. De Raedt and M. Bruynooghe. Belief updating from integrity constraints and queries. Artificial Intelligence, 53:291–307, 1992.

    Article  Google Scholar 

  12. L. De Raedt and M. Bruynooghe. A theory of clausal discovery. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1993.

    Google Scholar 

  13. L. De Raedt, N. Lavrač, and S. DŽeroski. Multiple predicate learning. In S. Muggleton, editor, Proceedings of the 3rd International Workshop on Inductive Logic Programming, pages 221–240. J. Stefan Institute, 1993.

    Google Scholar 

  14. L. De Raedt and W. Van Lear. Inductive constraint logic. In Proceedings of the 5th International Workshop on Algorithmic Learning Theory, 1995.

    Google Scholar 

  15. M. Denecker, L. De Raedt, P. Flach, and A. Kakas, editors. Proceedings of ECAI96 Workshop on Abductive and Inductive Reasoning. Catholic University of Leuven, 1996.

    Google Scholar 

  16. Y. Dimopoulos and A. Kakas. Learning Non-monotonic Logic Programs: Learning Exceptions. In Proceedings of the 8th European Conference on Machine Learning, 1995.

    Google Scholar 

  17. Y. Dimopoulos and A. Kakas. Abduction and inductive learning. In Advances in Inductive Logic Programming. IOS Press, 1996.

    Google Scholar 

  18. P.M. Dung. Negation as hypothesis: An abductive foundation for logic programming. In K. Furukawa, editor, Proceedings of the 8th International Conference on Logic Programming, pages 3–17. MIT Press, 1991.

    Google Scholar 

  19. S. DŽeroski. Handling noise in inductive logic programming. Master's thesis, Faculty of Electrical Engineering and Computer Science, University of Ljubljana, 1991.

    Google Scholar 

  20. K. Eshghi and R.A. Kowalski. Abduction compared with Negation by Failure. In Proceedings of the 6th International Conference on Logic Programming, 1989.

    Google Scholar 

  21. F. Esposito, E. Lamma, D. Malerba, P. Mello, M. Milano, F. Riguzzi, and G. Semeraro. Learning abductive logic programs. In Denecker et al. [15].

    Google Scholar 

  22. C. Hartshorne and P. Weiss, editors. Collected Papers of Charles Sanders Peirce, 1931–1958, volume 2. Harvards University Press, 1965.

    Google Scholar 

  23. A.C. Kakas, R.A. Kowalski, and F. Toni. Abductive logic programming. Journal of Logic and Computation, 2:719–770, 1993.

    MathSciNet  Google Scholar 

  24. A.C. Kakas and P. Mancarella. On the relation between truth maintenance and abduction. In Proceedings of the 2nd Pacific Rim International Conference on Artificial Intelligence, 1990.

    Google Scholar 

  25. A.C. Kakas, P. Mancarella, and P.M. Dung. The acceptability semantics for logic programs. In Proceedings of the 11th International Conference on Logic Programming, 1994.

    Google Scholar 

  26. A.C. Kakas and F. Riguzzi. Learning with abduction. Technical Report TR-96-15, University of Cyprus, Computer Science Department, 1996.

    Google Scholar 

  27. A.C. Kakas and F. Riguzzi. Learning with abduction. In Proceedings of the 7th International Workshop on Inductive Logic Programming, 1997.

    Google Scholar 

  28. E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Integrating induction and abduction in logic programming. To appear on Information Sciences.

    Google Scholar 

  29. E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Integrating Induction and Abduction in Logic Programming. In P. P. Wang, editor, Proceedings of the Third Joint Conference on Information Sciences, volume 2, pages 203–206, 1997.

    Google Scholar 

  30. E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Introducing Abduction into (Extensional) Inductive Logic Programming Systems. In M. Lenzerini, editor, AI * IA97, Advances in Artificial Intelligence, Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence, number 1321 in LNAI. Springer-Verlag, 1997.

    Google Scholar 

  31. N. Lavrač and S. DŽeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.

    Google Scholar 

  32. L. Martin and C. Vrain. A three-valued framework for the induction of general logic programs. In Advances in Inductive Logic Programming. IOS Press, 1996.

    Google Scholar 

  33. R. Michalski, J.G. Carbonell, and T.M. Mitchell (eds). Machine Learning — An Artificial Intelligence Approach. Springer-Verlag, 1984.

    Google Scholar 

  34. S. Muggleton. Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming, 13(3–4):245–286, 1995.

    Google Scholar 

  35. L. M. Pereira, C. V. Damásio, and J. J. Alferes. Diagnosis and debugging as contradiction removal. In L. M. Pereira and A. Nerode, editors, Proceedings of the 2nd International Workshop on Logic Programming and Non-monotonic Reasoning, pages 316–330. MIT Press, 1993.

    Google Scholar 

  36. D.L. Poole. A logical framework for default reasoning. Artificial Intelligence, 32, 1988.

    Google Scholar 

  37. J. R. Quinlan and R.M. Cameron-Jones. Induction of Logic Programs: FOIL and Related Systems. New Generation Computing, 13:287–312, 1995.

    Article  Google Scholar 

  38. E. Shapiro. Algorithmic Program Debugging. MIT Press, 1983.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DEIS, Università di Bologna, Viale Risorgimento 2, I-40136, Bologna, Italy

    Evelina Lamma, Michela Milano & Fabrizio Riguzzi

  2. Dip. di Ingegneria, Università di Ferrara, Via Saragat 1, I-44100, Ferrara, Italy

    Paola Mello

Authors
  1. Evelina Lamma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paola Mello
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michela Milano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabrizio Riguzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Jürgen Dix Luís Moniz Pereira Teodor C. Przymusinski

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lamma, E., Mello, P., Milano, M., Riguzzi, F. (1998). A system for abductive learning of logic programs. In: Dix, J., Pereira, L.M., Przymusinski, T.C. (eds) Logic Programming and Knowledge Representation. LPKR 1997. Lecture Notes in Computer Science, vol 1471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054792

Download citation

  • DOI: https://doi.org/10.1007/BFb0054792

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64958-8

  • Online ISBN: 978-3-540-49872-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation