Advertisement

Macro-Operators Revisited in Inductive Logic Programming

  • Érick Alphonse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3194)

Abstract

For the last ten years a lot of work has been devoted to propositionalization techniques in relational learning. These techniques change the representation of relational problems to attribute-value problems in order to use well-known learning algorithms to solve them. Propositionalization approaches have been successively applied to various problems but are still considered as ad hoc techniques. In this paper, we study these techniques in the larger context of macro-operators as techniques to improve the heuristic search. The macro-operator paradigm enables us to propose a unified view of propositionalization and to discuss its current limitations. We show that a whole new class of approaches can be developed in relational learning which extends the idea of changes of representation to more suited learning languages. As a first step, we propose different languages that provide a better compromise than current propositionalization techniques between the cost of building macro-operators and the cost of learning. It is known that ILP problems can be reformulated either into attribute-value or multi-instance problems. With the macro-operator approach, we see that we can target a new representation language we name multi-table. This new language is more expressive than attribute-value but is simpler than multi-instance. Moreover, it is PAC-learnable under weak constraints. Finally, we suggest that relational learning can benefit from both the problem solving and the attribute-value learning community by focusing on the design of effective macro-operator approaches.

Keywords

Representation Language Inductive Logic Inductive Logic Programming Hypothesis Base Relational Learning 
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. 1.
    Mitchell, T.M.: Generalization as search. Artificial Intelligence 18, 203–226 (1982)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Haussler, D.: Learning conjunctive concepts in structural domains. Machine Learning 4, 7–40 (1989)Google Scholar
  3. 3.
    Cohen, W.W.: Learnability of restricted logic programs. In: Muggleton, S. (ed.) Proceedings of the 3rd International Workshop on Inductive Logic Programming, J. Stefan Institute, pp. 41–72 (1993)Google Scholar
  4. 4.
    Kearns, M.J., Vazirani, U.V.: An Introduction to Computational Learning Theory. The MIT Press, Cambridge (1994)Google Scholar
  5. 5.
    Quinlan, J.R.: Determining literals in inductive logic programming. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence, Sydney, Austalia, pp. 746–750 (1991)Google Scholar
  6. 6.
    Silverstein, G., Pazzani, M.J.: Relational cliches: Constraining constructive induction during relational learning. In: Birnbaum, L., Collins, G. (eds.) Proceedings of the 8th International Workshop on Machine Learning, pp. 203–207. Morgan Kaufmann, San Francisco (1991)Google Scholar
  7. 7.
    Richards, B., Mooney, R.: Learning relations by pathfinding. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 723–738 (1992)Google Scholar
  8. 8.
    Giordana, A., Saitta, L.: Phase transitions in learning relations. Machine Learning 41, 217–225 (2000)zbMATHCrossRefGoogle Scholar
  9. 9.
    Cheeseman, P., Kanefsky, B., Taylor, W.M.: Where the really hard problems are. In: Myopoulos, J., Reiter, R. (eds.) Proceedings of the 12th International Joint Conference on Artificial Intelligence, Sydney, Australia, pp. 331–340. Morgan Kaufmann, San Francisco (1991)Google Scholar
  10. 10.
    Fürnkranz, J., Flach, P.: An analysis of rule learning heuristics. Technical Report CSTR-03-002, Department of Computer Science, University of Bristol (2003)Google Scholar
  11. 11.
    Korf, R.E.: Macro-operators: a weak method for learning. Artificial Intelligence, 1985 26, 35–77 (1985)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Peña Castillo, L., Wrobel, S.: Macro-operators in multirelational learning: a searchspace reduction technique. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 357–368. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Michalski, R.S.: Pattern recognition as knowledge-guided computer induction. Technical Report 927, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois (1978)Google Scholar
  14. 14.
    Khan, K., Muggleton, S., Parson, R.: Repeat learning using predicate invention. In: Page, D.L. (ed.) ILP 1998. LNCS, vol. 1446, pp. 165–174. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  15. 15.
    Amarel, S.: On Representations of Problems of Reasoning about Actions, Alphonse, vol. 24 E, pp. 131–171. American Elsevier, New York (1968)Google Scholar
  16. 16.
    Kramer, S., Pfahringer, B., Helma, C.: Stochastic propositionalization of nondeterminate background knowledg. In: Page, D. (ed.) Proc. of the 8th International Workshop on Inductive Logic Programming, pp. 80–94. Springer, Heidelberg (1998)Google Scholar
  17. 17.
    Turney, P.: Low size-complexity inductive logic programming: The East-West challenge considered as a problem in cost-sensitive classification. In: Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 247–263 (1995)Google Scholar
  18. 18.
    Geibel, P., Wysotzki, F.: Learning context dependent concepts. In: Raedt, L.D. (ed.) Proceedings of the 5th International Workshop on Inductive Logic Programming, Department of Computer Science, Katholieke Universiteit Leuven, pp. 323–337 (1995)Google Scholar
  19. 19.
    Cumby, C., Roth, D.: Relational representations that facilitate learning. In: Cohn, A.G., Giunchiglia, F., Selman, B. (eds.) Proceedings of the Conference on Principles of Knowledge Representation and Reasoning (KR 2000), pp. 425–434. Morgan Kaufman Publishers, San Francisco (2000)Google Scholar
  20. 20.
    Lavrač, N., Flach, P.A.: An extended transformation approach to inductive logic programming. ACM Transactions on Computational Logic 2, 458–494 (2001)CrossRefGoogle Scholar
  21. 21.
    Krogel, M.A., Wrobel, S.: Transformation-based learning using multirelational aggregation. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 142–155. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  22. 22.
    Roth, D., Yih, W.: Relational learning via propositional algorithms: An information extraction case study. In: Nebel, B. (ed.) Proceedings of the seventeenth International Conference on Artificial Intelligence (IJCAI 2001), pp. 1257–1263. Morgan Kaufmann Publishers, Inc, San Francisco (2001)Google Scholar
  23. 23.
    Kramer, S., de Raedt, L.: Feature construction with version spaces for biochemical applications. In: Proc. 18th International Conf. on Machine Learning, pp. 258–265. Morgan Kaufmann, San Francisco (2001)Google Scholar
  24. 24.
    Hernádvölgyi, I.T.: Searching for macro operators with automatically generated heuristics. LNCS, vol. 2056, p. 194 (2001)Google Scholar
  25. 25.
    Lavrac, N., Dzeroski, S., Grobelnik, M.: Learning nonrecursive definitions of relations with LINUS. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS(LNAI), vol. 482, pp. 265–281. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  26. 26.
    Dzeroski, S., Muggleton, S., Russell, S.J.: PAC-learnability of determinate logic programs. In: Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (COLT 1992), Pittsburgh, Pennsylvania, ACM Press, New York (1992)Google Scholar
  27. 27.
    Zucker, J.D., Ganascia, J.G.: Selective reformulation of examples in concept learning. In: Proc. 11th International Conference on Machine Learning, pp. 352–360. Morgan Kaufmann, San Francisco (1994)Google Scholar
  28. 28.
    Sebag, M., Rouveirol, C.: Induction of maximally general clauses consistent with integrity constraints. In: Wrobel, S. (ed.) Proceedings of the 4th InternationalWorkshop on Inductive Logic Programming GMD-Studien., Gesellschaft für Mathematik und Datenverarbeitung MBH, vol. 237, pp. 195–216 (1994)Google Scholar
  29. 29.
    Fensel, D., Zickwolff, M., Wiese, M.: Are substitutions the better examples? Learning complete sets of clauses with Frog. In: Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 453–474 (1995)Google Scholar
  30. 30.
    Sebag, M., Rouveirol, C.: Resource-bounded relational reasoning: Induction and deduction through stochastic matching. Machine Learning 38, 41–62 (2000)zbMATHCrossRefGoogle Scholar
  31. 31.
    Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)zbMATHCrossRefGoogle Scholar
  32. 32.
    Kietz, J.U., Dzeroski, S.: Inductive logic programming and learnability. SIGART Bulletin 5, 22–32 (1994)CrossRefGoogle Scholar
  33. 33.
    Lavrač, N., Ďzeroski, S.: Inductive Logic Programming: techniques and Applications. Ellis Horwood (1994)Google Scholar
  34. 34.
    Nienhuys-Cheng, S.H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)Google Scholar
  35. 35.
    Sebag, M., Rouveirol, C.: Tractable induction and classification in first order logic via stochastic matching. In: 15th Int. Join Conf. on Artificial Intelligence (IJCAI 1997), pp. 888–893. Morgan Kaufmann, San Francisco (1997)Google Scholar
  36. 36.
    Dietterich, T.G., Michalski, R.S.: A comparative review of selected methods for learning from examples. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning, an Artificial Intelligence approach, vol. 1, pp. 41–81. Morgan Kaufmann, San Mateo (1983)Google Scholar
  37. 37.
    Sebag, M., Rouveirol, C.: Constraint inductive logic programming. In: Raedt, L.D. (ed.) Proceedings of the 5th International Workshop on Inductive Logic Programming, Department of Computer Science, Katholieke Universiteit Leuven, pp. 181–198 (1995)Google Scholar
  38. 38.
    Anthony, S., Frisch, A.M.: Generating numerical literals during refinement. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS(LNAI), vol. 1297, pp. 61–76. Springer, Heidelberg (1997)Google Scholar
  39. 39.
    Botta, M., Piola, R.: Refining numerical constants in first order logic theories. Machine Learning 38, 109–131 (2000)zbMATHCrossRefGoogle Scholar
  40. 40.
    Rouveirol, C.: Flattening and saturation: Two representation changes for generalization. Machine Learning 14, 219–232 (1994)zbMATHCrossRefGoogle Scholar
  41. 41.
    Liquière, M.: Du structurel au propositionnel, une approche formelle. In: CAP, Grenoble (2001)Google Scholar
  42. 42.
    Kietz, J.U., Lübbe, M.: An efficient subsumption algorithm for inductive logic programming. In: Cohen, W.W., Hirsh, H. (eds.) Proc. 11th International Conference on Machine Learning, pp. 130–138. Morgan Kaufmann, San Francisco (1994)Google Scholar
  43. 43.
    Gottlob, G., Leone, N., Scarello, F.: A comparison of structural CSP decomposition methods. Artificial Intelligence 124, 243–282 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  44. 44.
    de Raedt, L.: Attribute-value learning versus inductive logic programming: The missing link. In: Page, D. (ed.) Proc. of the 8th InternationalWorkshop on Inductive Logic Programming, pp. 1–8. Springer, Heidelberg (1998) (extended abstract)CrossRefGoogle Scholar
  45. 45.
    Chevaleyre, Y., Zucker, J.D.: A framework for learning rules from multiple instance data. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 49–60. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  46. 46.
    Pitt, L., Warmuth, M.K.: Prediction preserving reducibility. J. of Comput. Syst. Sci. 41, 430–467 (1990) ;Special issue of the for the Third Annual Conference of Structure in Complexity Theory ,Washington, DC., (June 1988)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Érick Alphonse
    • 1
  1. 1.MIG INRA/UR1077Jouy en Josas CedexFrance

Personalised recommendations