Advertisement

Learning multiple predicates

  • Antonis Kakas
  • Evelina Lamma
  • Fabrizio Riguzzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1480)

Abstract

We present an approach for solving some of the problems of top-down Inductive Logic Programming systems when learning multiple predicates. The approach is based on an algorithm for learning abductive logic programs. Abduction is used to generate additional information that is useful for solving the problem of global inconsistency when learning multiple predicates.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    H. Adé and M. Denecker. AILP: Abductive inductive logic programming. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1995.Google Scholar
  2. 2.
    F. Bergadano and D. Gunetti. Inductive Logic Programming: From Machine Learning to Software Engineering. The MIT Press, 1995.Google Scholar
  3. 3.
    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.MATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    L. De Raedt and L. Dehaspe. Learning from satisfiability. Technical report, Katholieke Universiteit Leuven, 1996.Google Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Y. Dimopoulos and A. Kakas. Abduction and inductive learning. In Advances in Inductive Logic Programming. IOS Press, 1996.Google Scholar
  8. 8.
    N. Inuzuka, M. Kamo, N. Ishii, H. Seki, and H. Itoh. Top-down induction of logic programs from incomplete samples. In S. Muggleton, editor, Proceedings of the 6th International Workshop on Inductive Logic Programming, number 1314 in LNAI, pages 265–284. Springer-Verlag, 1997.Google Scholar
  9. 9.
    A.C. Kakas, R.A. Kowalski, and F. Toni. The role of abduction in logic programming. In D. Gabbay, C. Hogger, and J. Robinson, editors, Handbook of Logic in AI and Logic Programming, volume 5, pages 233–306. Oxford University Press, 1997.Google Scholar
  10. 10.
    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
  11. 11.
    A.C. Kakas and F. Riguzzi. Learning with abduction. In Proceedings of the 7th International Workshop on Inductive Logic Programming, 1997.Google Scholar
  12. 12.
    E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Integrating induction and abduction in logic programming. To appear on Information Sciences.Google Scholar
  13. 13.
    E. Lamma, P. Mello, M. Milano, and F. Riguzzi. Integrating extensional and intensional ILP systems through abduction. In Proceedings of the 7th International Workshop on Logic Program Synthesis and Transformation, 1997.Google Scholar
  14. 14.
    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
  15. 15.
    L. Martin and C. Vrain. MULT_ICN: An empirical multiple predicate learner. In L. De Raedt, editor, Proceedings of the 5th International Workshop on Inductive Logic Programming, pages 129–144. Department of Computer Science, Katholieke Universiteit Leuven, 1995.Google Scholar
  16. 16.
    L. Martin and C. Vrain. A three-valued framework for the induction of general program. In L. De Raedt, editor, Proceedings of the 5th International Workshop on Inductive Logic Programming, pages 109–128. Department of Computer Science, Katholieke Universiteit Leuven, 1995.Google Scholar
  17. 17.
    M.J. Pazzani and D. Kibler. The utility of knowledge in inductive learning. Machine Learning, 9(1):57–94, 1992.Google Scholar
  18. 18.
    J. Pearl. Embracing causality in formal reasoning. In Proceedings of the 6th National Conference on Artificial Intelligence, pages 369–373, Seattle, WA, 1987.Google Scholar
  19. 19.
    J. R. Quinlan and R.M. Cameron-Jones. Induction of Logic Programs: FOIL and Related Systems. New Generation Computing, 13:287–312, 1995.CrossRefGoogle Scholar
  20. 20.
    E. Shapiro. Algorithmic Program Debugging. MIT Press, 1983.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Antonis Kakas
    • 1
  • Evelina Lamma
    • 2
  • Fabrizio Riguzzi
    • 2
  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus
  2. 2.DEISUniversità di BolognaBolognaItaly

Personalised recommendations