Towards a theory of inductive logic programming

  • Peter A. Flach
Communications Logic For Artificial Intelligence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 542)


We propose a theoretical framework for Inductive Logic Programming, which contains the notion of explanation as a parameter. This enables us to vary the logic in which the induced theory is expressed, and it also allows us to introduce the notion of weak explanation, which can be used to address novel induction problems. We illustrate the usefulness of this framework by several examples.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. K.L. Clark (1978), ‘Negation as failure'. In Logic and Databases, H. Gallaire & J. Minker (eds.), pp. 293–322, Plenum Press, New York.Google Scholar
  2. P.A. Flach (1990a), ‘Second-order inductive learning', ITK Research Report no. 10, Institute for Language Technology & Artificial Intelligence, Tilburg University, the Netherlands, January. A preliminary version of this paper appeared in Analogical and Inductive Inference AII'89, K.P. Jantke (ed.), Lecture Notes in Computer Science 397, Springer Verlag, Berlin, 1989, pp. 202–216.Google Scholar
  3. P.A. Flach (1990b), ‘Inductive characterisation of database relations'. In Proc. International Symposium on Methodologies for Intelligent Systems, Z.W. Ras, M. Zemankowa & M.L. Emrich (eds.), pp. 371–378, North-Holland, Amsterdam. Full version appeared as ITK Research Report no. 23.Google Scholar
  4. P.A. Flach (1991), ‘The logic of explanations'. In preparation.Google Scholar
  5. D.M. Gabbay (1985), ‘Theoretical foundations for non-monotonic reasoning in expert systems'. In Logics and models of concurrent systems, K.R. Apt (ed.), pp. 439–457, Springer-Verlag, Berlin.Google Scholar
  6. S. Kraus, D. Lehmann & M. Magidor (1990), ‘Nonmonotonic reasoning, preferential models and cumulative logics', Artificial Intelligence44, pp. 167–207.Google Scholar
  7. T.M. Mitchell (1982), ‘Generalization as search', Artificial Intelligence18:2, pp. 203–226.Google Scholar
  8. S. Muggleton (1987), ‘Duce, an oracle based approach to constructive induction'. In Proc. Tenth International Joint Conference on Artificial Intelligence, pp. 287–292, Morgan Kaufmann, Los Altos, CA.Google Scholar
  9. S. Muggleton (1990), ‘Inductive Logic Programming'. In Proc. First Conference on Algorithmic Learning Theory, Ohmsha, Tokyo.Google Scholar
  10. S. Muggleton, ed. (1991), Proc. First International Workshop on Inductive Logic Programming, Viana de Castelo, Portugal.Google Scholar
  11. S. Muggleton & W. Buntine (1988), ‘Machine invention of first-order predicates by inverting resolution'. In Proc. Fifth International Conference on Machine Learning, J. Laird (ed.), pp. 339–352, Morgan Kaufmann, San Mateo.Google Scholar
  12. D. Poole (1989), ‘Normality and faults in logic-based diagnosis'. In Proc. Eleventh International Joint Conference on Artificial Intelligence, pp. 1304–1310, Morgan Kaufmann, Los Altos, CA.Google Scholar
  13. L. de Raedt (1991), Interactive concept-learning, PhD thesis, Catholic University Leuven.Google Scholar
  14. E.Y. Shapiro (1983), Algorithmic program debugging, MIT Press.Google Scholar
  15. W. Zadrozny (1990), ‘The logic of abduction (preliminary report)'. In Proc. First International Workshop on Principles of Diagnosis, pp. 8–17, Stanford University.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Peter A. Flach
    • 1
  1. 1.Institute for Language Technology and Artificial IntelligenceTilburg UniversityLE TilburgThe Netherlands

Personalised recommendations