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The many faces of inductive logic programming

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Methodologies for Intelligent Systems (ISMIS 1993)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 689))

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Abstract

Inductive logic programming is a research area which has its roots in machine learning and computational logic. A short introduction to this area is given. It investigates the many faces of inductive logic programming and outlines their applications in knowledge discovery and programming. Furthermore, whereas most research in inductive logic programming has focussed on learning single predicates from given datasets using a strong notion of explanation (cf. the well-known systems GOLEM and FOIL), we also investigate a weaker notion of explanation and the learning of multiple predicates. The weaker setting avoids the order dependency problem of the strong notion when learning multiple predicates, extends the representation of the induced hypotheses to full clausal logic, and can be applied to different types of application.

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References

  1. I. Bratko, S. Muggleton, and A. Varsek. Learning qualitative models of dynamic systems. In Proceedings of the 8th International Workshop on Machine Learning, pages 385–388. Morgan Kaufmann, 1991.

    Google Scholar 

  2. Wray Buntine. Generalized subsumption and its application to induction and redundancy. Artificial Intelligence, 36:375–399, 1988.

    Google Scholar 

  3. K. L. Clark. Negation as failure. In H. Gallaire and J. Minker, editors, Logic and Databases, pages 293–322. Plenum Press, 1978.

    Google Scholar 

  4. L. De Raedt. Interactive Theory Revision: an Inductive Logic Programming Approach. Academic Press, 1992.

    Google Scholar 

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

    Google Scholar 

  6. L. De Raedt and M. Bruynooghe. A unifying framework for concept-learning algorithms. The Knowledge Engineering Review, 7(3):251–269, 1992.

    Google Scholar 

  7. L. De Raedt and M. Bruynooghe. A theory of clausal discovery. Technical Report KUL-CW-164, Department of Computer Science, Katholieke Universiteit Leuven, 1993. to appear in Proceedings of the 3rd International Workshop on Inductive Logic Programming.

    Google Scholar 

  8. L. De Raedt, N. Lavrač, and S. Džeroski. Multiple predicate learning. Technical Report KUL-CW-165, Department of Computer Science, Katholieke Universiteit Leuven, 1993. to appear in Proceedings of the 3rd International Workshop on Inductive Logic Programming.

    Google Scholar 

  9. B. Dolsak and S. Muggleton. The application of inductive logic programming to finite element mesh design. In S. Muggleton, editor, Inductive Logic Programming, pages 453–472. Academic Press, 1992.

    Google Scholar 

  10. S. Džeroski and I. Bratko. Handling noise in inductive logic programming. In S. Muggleton, editor, Proceedings of the 2nd International Workshop on Inductive Logic Programming, 1992.

    Google Scholar 

  11. C. Feng. Inducing temporal fault diagnostic rules from a qualitative model. In Proceedings of the 8th International Workshop on Machine Learning, pages 403–406. Morgan Kaufmann, 1991.

    Google Scholar 

  12. P. Flach. A framework for inductive logic programming. In S. Muggleton, editor, Inductive logic programming. Academic Press, 1992.

    Google Scholar 

  13. N. Helft. Induction as nonmonotonic inference. In Proceedings of the 1st International Conference on Principles of Knowledge Representation and Reasoning, pages 149–156. Morgan Kaufmann, 1989.

    Google Scholar 

  14. J-U. Kietz and S. Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, 1992.

    Google Scholar 

  15. 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 structureactivity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences, 1992.

    Google Scholar 

  16. R. Korf. Depth-first iterative deepening: an optimal admissable search. Artificial Intelligence, 1985.

    Google Scholar 

  17. N. Lavrač and S. Džeroski. Inductive learning of relations from noisy examples. In Muggleton S., editor, Inductive Logic Programming Workshop, pages 495–514. Academic Press, 1992.

    Google Scholar 

  18. N. Lavrač, S. Džeroski, and M. Grobelnik. Learning non-recursive definitions of relations with LINUS. In Yves Kodratoff, editor, Proceedings of the 5th European Working Session on Learning, volume 482 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1991.

    Google Scholar 

  19. J.W. Lloyd. Foundations of logic programming. Springer-Verlag, 2nd edition, 1987.

    Google Scholar 

  20. T.M. Mitchell. Generalization as search. Artificial Intelligence, 18:203–226, 1982.

    Google Scholar 

  21. S. Muggleton. Inductive logic programming. New Generation Computing, 8(4):295–317, 1991.

    Google Scholar 

  22. S. Muggleton, editor. Inductive Logic Programming. Academic Press, 1992.

    Google Scholar 

  23. 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 

  24. S. Muggleton and W. Buntine. Machine invention of first order predicates by inverting resolution. In Proceedings of the 5th International Conference on Machine Learning, pages 339–351. Morgan Kaufmann, 1988.

    Google Scholar 

  25. S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the 1st conference on algorithmic learning theory. Ohmsma, Tokyo, Japan, 1990.

    Google Scholar 

  26. S. Muggleton, R.D. King, and M.J.E. Sternberg. Protein secondary structure prediction using logic. Protein Engineering, 7:647–657, 1992.

    Google Scholar 

  27. T. Niblett. A study of generalisation in logic programs. In D. Sleeman, editor, Proceedings of the 3rd European Working Session On Learning, pages 131–138. Pitman, 1988.

    Google Scholar 

  28. G. Piatetsky-Shapiro and W. Frawley, editors. Knowledge discovery in databases. The MIT press, 1991.

    Google Scholar 

  29. G. Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5. Edinburgh University Press, 1970.

    Google Scholar 

  30. J.R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.

    Google Scholar 

  31. J.R. Quinlan. Learning logical definition from relations. Machine Learning, 5:239–266, 1990.

    Google Scholar 

  32. R. Reiter. On asking what a database knows. In J.W. Lloyd, editor, Computational Logic, pages 96–113. Springer-Verlag, 1990.

    Google Scholar 

  33. S.J. Russell. The use of knowledge in analogy and induction. Pitman, 1989.

    Google Scholar 

  34. E.Y. Shapiro. Algorithmic Program Debugging. The MIT press, 1983.

    Google Scholar 

  35. M.E. Stickel. A prolog technology theorem prover: implementation by an extended prolog compiler. Journal of Automated Reasoning, 4(4):353–380, 1988.

    Google Scholar 

  36. C. Vermeulen. Een toepassing van automatisch leren op weersvoorspellingen. Master's thesis, Department of Computer Science, Katholieke Universiteit Leuven, 1992. in Dutch.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computing Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, B-3001, Heverlee, Belgium

    Luc De Raedt

  2. Jožef Stefan Institute, Jamova 39, 61111, Ljubljana, Slovenia

    Nada Lavrač

Authors
  1. Luc De Raedt
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  2. Nada Lavrač
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Jan Komorowski Zbigniew W. Raś

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© 1993 Springer-Verlag Berlin Heidelberg

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De Raedt, L., Lavrač, N. (1993). The many faces of inductive logic programming. In: Komorowski, J., Raś, Z.W. (eds) Methodologies for Intelligent Systems. ISMIS 1993. Lecture Notes in Computer Science, vol 689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56804-2_41

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  • DOI: https://doi.org/10.1007/3-540-56804-2_41

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56804-9

  • Online ISBN: 978-3-540-47750-1

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