Advertisement

Inductive constraint logic

  • Luc De Raedt
  • Wim Van Laer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 997)

Abstract

A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is a special case of the former. Because of this property, we are able to adapt AQ and CN2 type algorithms in order to enable learning of full first order formulae. However, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form, we will use a clausal representation, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples. This representation duality reverses also the role of positive and negative examples, both in the heuristics and in the algorithm. The resulting theory is incorporated in a system named ICL (Inductive Constraint Logic).

Keywords

Conjunctive Normal Form Inductive Logic Inductive Logic Programming Disjunctive Normal Form Target Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Adé et al., 1995]
    H. Adé, L. De Raedt, and M. Bruynooghe. Declarative Bias for Specific-To-General ILP Systems. Machine Learning, 1995. To appear.Google Scholar
  2. [Clark and Niblett, 1989]
    P. Clark and T. Niblett. The CN2 algorithm. Machine Learning, 3(4):261–284, 1989.Google Scholar
  3. [De Raedt and Džeroski, 1994]
    L. De Raedt and S. Džeroski. First order jk-clausal theories are pac-learnable. Artificial Intelligence, 70:375–392, 1994.CrossRefGoogle Scholar
  4. [De Raedt et al., 1993]
    L. De Raedt, N. Lavrač, and S. Džeroski. Multiple predicate learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1037–1042. Morgan Kaufmann, 1993.Google Scholar
  5. [Dietterich and Michalski, 1985]
    T.G. Dietterich and R.S. Michalski. Discovering patterns in sequences of events. Artificial Intelligence, 25:257–294, 1985.Google Scholar
  6. [Genesereth and Nilsson, 1987]
    M. Genesereth and N. Nilsson. Logical foundations of artificial intelligence. Morgan Kaufmann, 1987.Google Scholar
  7. [Haussler, 1988]
    D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artificial Intelligence, 36:177–221, 1988.CrossRefGoogle Scholar
  8. [King et al., 1992]
    R.D. King, S. Muggleton, R.A. Lewis, and M.J.E. Sternberg. Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences, 89(23), 1992.Google Scholar
  9. [Lavrač and Džeroski, 1994]
    N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.Google Scholar
  10. [Lloyd, 1987]
    J.W. Lloyd. Foundations of logic programming. Springer-Verlag, 2nd edition, 1987.Google Scholar
  11. [Michalski and Stepp, 1983]
    R.S. Michalski and R.E. Stepp. Learning from observation: conceptual clustering. In R.S Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: an artificial intelligence approach, volume 1. Tioga Publishing Company, 1983.Google Scholar
  12. [Michalski, 1983]
    R.S. Michalski. A theory and methodology of inductive learning. In R.S Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: an artificial intelligence approach, volume 1. Morgan Kaufmann, 1983.Google Scholar
  13. [Mooney, 1995]
    R.J. Mooney. Encouraging experimental results on learning cnf. Machine Learning, 19:79–92, 1995.Google Scholar
  14. [Muggleton and De Raedt, 1994]
    S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19, 20:629–679, 1994.CrossRefGoogle Scholar
  15. [Muggleton et al., 1989]
    S. Muggleton, M. Bain, J. Hayes-Michie, and D. Michie. An experimental comparison of human and machine learning formalisms. In Proceedings of the 6th International Workshop on Machine Learning, pages 113–118. Morgan Kaufmann, 1989.Google Scholar
  16. [Muggleton, 1992]
    S. Muggleton, editor. Inductive Logic Programming. Academic Press, 1992.Google Scholar
  17. [Plotkin, 1970]
    G. Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5, pages 153–163. Edinburgh University Press, 1970.Google Scholar
  18. [Quinlan, 1986]
    J.R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.Google Scholar
  19. [Quinlan, 1990]
    J.R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239–266, 1990.Google Scholar
  20. [Rouveirol, 1994]
    C. Rouveirol. Flattening and saturation: Two representation changes for generalization. Machine Learning, 14:219–232, 1994.CrossRefGoogle Scholar
  21. [Shapiro, 1983]
    E.Y. Shapiro. Algorithmic Program Debugging. The MIT Press, 1983.Google Scholar
  22. [Srinivasan et al., 1994]
    A. Srinivasan, S.H. Muggleton, R.D. King, and M.J.E. Sternberg. Mutagenesis: Ilp experiments in a non-determinate biological domain. In S. Wrobel, editor, Proceedings of the 4th International Workshop on Inductive Logic Programming, volume 237 of GMD-Studien, pages 217–232. Gesellschaft für Mathematik und Datenverarbeitung MBH, 1994.Google Scholar
  23. [van der Laag and Nienhuys-Cheng, 1993]
    P.R.J. van der Laag and S.-H. Nienhuys-Cheng. Subsumption and refinement in model inference. In P. Brazdil, editor, Proceedings of the 6th European Conference on Machine Learning, volume 667 of Lecture Notes in Artificial Intelligence, pages 95–114. Springer-Verlag, 1993.Google Scholar
  24. [Van Laer et al., 1994]
    W. Van Laer, L. Dehaspe, and L. De Raedt. Applications of a logical discovery engine. In Proceedings of the AAAI Workshop on Knowledge Discovery in Databases, pages 263–274, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Luc De Raedt
    • 1
  • Wim Van Laer
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium

Personalised recommendations