Bottom-Up ILP Using Large Refinement Steps

  • Marta Arias
  • Roni Khardon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3194)


The LogAn-H system is a bottom up ILP system for learning multi-clause and multi-predicate function free Horn expressions in the framework of learning from interpretations. The paper introduces a new implementation of the same base algorithm which gives several orders of magnitude speedup as well as extending the capabilities of the system. New tools include several fast engines for subsumption tests, handling real valued features, and pruning. We also discuss using data from the standard ILP setting in our framework, which in some cases allows for further speedup. The efficacy of the system is demonstrated on several ILP datasets.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bianchetti, J.A., Rouveirol, C., Sebag, M.: Constraint-based learning of long relational concepts. In: Proceedings of the International Conference on Machine Learning, pp. 35–42. Morgan Kaufmann, San Francisco (2002)Google Scholar
  2. 2.
    Blockeel, H., De Raedt, L.: Lookahead and discretization in ilp. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 77–84. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Blockeel, H., De Raedt, L.: Top down induction of first order logical decision trees. Artificial Intelligence 101, 285–297 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Bratko, I., Muggleton, S.: Applications of inductive logic programming. Communications of the ACM 38(11), 65–70 (1995)CrossRefGoogle Scholar
  5. 5.
    De Raedt, L.: Logical settings for concept learning. Artificial Intelligence 95(1), 187–201 (1997) ;See also relevant Errata (forthcoming)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    De Raedt, L., Dzeroski, S.: First order jk-clausal theories are PAC-learnable. Artificial Intelligence 70, 375–392 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    De Raedt, L., Van Laer, W.: Inductive constraint logic. In: Zeugmann, T., Shinohara, T., Jantke, K.P. (eds.) ALT 1995. LNCS, vol. 997, Springer, Heidelberg (1995)Google Scholar
  8. 8.
    Di Mauro, N., Basile, T.M.A., Ferilli, S., Esposito, F., Fanizzi, N.: An exhaustive matching procedure for the improvement of learning efficiency. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 112–129. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: International Conference on Machine Learning, pp. 194–202 (1995)Google Scholar
  10. 10.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the International Conference on Machine Learning, Amherst, MA, pp. 1022–1027 (1993)Google Scholar
  11. 11.
    Giordana, A., Saitta, L., Sebag, M., Botta, M.: Relational learning as search in a critical region. Journal of Machine Learning Research 4, 431–463 (2003)MathSciNetGoogle Scholar
  12. 12.
    Khardon, R.: Learning function free Horn expressions. Machine Learning 37, 241–275 (1999)zbMATHCrossRefGoogle Scholar
  13. 13.
    Khardon, R.: Learning horn expressions with LogAn-H. In: Proceedings of the International Conference on Machine Learning, pp. 471–478. Morgan Kaufmann, San Francisco (2000)Google Scholar
  14. 14.
    Kietz, J.-U., Lübbe, M.: An efficient subsumption algorithm for inductive logic programming. In: Wrobel, S. (ed.) Proceedings of the 4th International Workshop on Inductive Logic Programming Gesellschaft für Mathematik und Datenverarbeitung MBH, vol. 237, pp. 97–106 (1994)Google Scholar
  15. 15.
    Van Laer, W., Blockeel, H., De Raedt, L.: Inductive constraint logic and the mutagenesis problem. In: Proceedings of the Eighth Dutch Conference on Artificial Intelligence, pp. 265–276 (November 1996)Google Scholar
  16. 16.
    Maloberti, J., Sebag, M.: Theta-subsumption in a constraint satisfaction perspective. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 164–178. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  18. 18.
    Muggleton, S.: Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4), 245–286 (1995)Google Scholar
  19. 19.
    Muggleton, S., Buntine, W.: Machine invention of first order predicates by inverting resolution. In: Muggleton, S. (ed.) Inductive Logic Programming, Academic Press, London (1992)Google Scholar
  20. 20.
    Muggleton, S., DeRaedt, L.: Inductive logic programming: Theory and methods. The Journal of Logic Programming 19&20, 629–680 (1994)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Muggleton, S., Feng, C.: Efficient induction of logic programs. In: Muggleton, S. (ed.) Inductive Logic Programming, pp. 281–298. Academic Press, London (1992)Google Scholar
  22. 22.
    Muggleton, S.H., Bain, M., Hayes-Michie, J., Michie, D.: An experimental comparison of human and machine learning formalisms. In: Proc. Sixth International Workshop on Machine Learning, pp. 113–118. Morgan Kaufmann, San Mateo (1989)Google Scholar
  23. 23.
    Papadimitriou, C.H., Yannakakis, M.: On the complexity of database queries (extended abstract). In: Proceedings of the 16th Annual ACM Symposium on Principles of Database Systems, pp. 12–19. ACM Press, New York (1997)Google Scholar
  24. 24.
    Plotkin, G.D.: A note on inductive generalization. Machine Intelligence 5, 153–163 (1970)MathSciNetGoogle Scholar
  25. 25.
    Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)Google Scholar
  26. 26.
    Sebag, M., Rouveirol, C.: Resource-bounded relational reasoning: Induction and deduction through stochastic matching. Machine Learning 38, 41–62 (2000)zbMATHCrossRefGoogle Scholar
  27. 27.
    Selman, B., Kautz, H., Cohen, B.: Local search strategies for satisfiability testing. In: Cliques, Coloring, and Satisfiability: the Second DIMACS Implementation Challenge, vol. 26, pp. 521–532. American Mathematical Society, Providence (1996)Google Scholar
  28. 28.
    Srinivasan, A., Muggleton, S., King, R.D.: Comparing the use of background knowledge by inductive logic programming systems. In: Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 199–230 (1995)Google Scholar
  29. 29.
    Srinivasan, A., Muggleton, S.H., King, R.D., Sternberg, M.J.E.: Mutagenesis: ILP experiments in a non-determinate biological domain. In: Wrobel, S. (ed.) Proc. 4th Int. Workshop on Inductive Logic Programming, pp. 217–232 (September 1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Marta Arias
    • 1
  • Roni Khardon
    • 1
  1. 1.Department of Computer ScienceTufts UniversityMedfordUSA

Personalised recommendations