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Some thoughts on the role of examples in program transformation and its relevance for explanation-based learning

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Book cover Analogical and Inductive Inference (AII 1989)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 397))

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Abstract

Explanation-based learning is using the same technique as partial evaluation, namely unfolding. However, it brings a new insight: an example can be used to guide the transformation process. In this paper, we further explore this insight and show how examples can be used to guide other kinds of program transformation, guiding not only the unfolding, but also the introduction of new predicates and the folding. On the other hand, we illustrate the more fundamental restructuring which is possible with program transformation and the relevance of completeness results to eliminate computationally inefficient knowledge.

Supported by the Belgian National Fund for Scientific Research.

Supported by the Belgian I.W.O.N.L.-I.R.S.I.A. under contract number 5203.

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References

  1. Bird R.S., Tabulation techniques for recursive programs, ACM Computing Surveys, Vol.12, No.4, 1980, pp.:403–417.

    Google Scholar 

  2. Bruynooghe M., A practical framework for the abstract interpretation of logic programs, Journal Logic Programming, 1989, to appear.

    Google Scholar 

  3. Bruynooghe M., De Schreye D. and Krekels B., Compiling Control, in Proc. Third International Symposium on Logic Programming, 1986, pp.70–78.

    Google Scholar 

  4. Bruynooghe M., De Raedt L., De Schreye D., Explanation-based program transformation, in Proc. International Joint Conf. Artificial Intelligence (IJCAI89), 1989, to appear.

    Google Scholar 

  5. Bruynooghe M., De Schreye D. and Krekels B., Compiling Control, Journal Logic Programming, 1989, pp: 135–162.

    Google Scholar 

  6. Burstall R.M., Darlington J., A transformation system for developing recursive programs, JACM, 24, 1977, pp. 44–67.

    Google Scholar 

  7. Chang C., Lee R.C., Symbolic Logic and Mechanical Theorem Proving, Academic Press Inc., 1973.

    Google Scholar 

  8. Debray S.K. Warren, D.S., Detection and optimisation of functional computations in Prolog, in Proc. Third International Logic Programming Conference, LNCS Vol.225, Springer Verlag, 1986, pp. 490–504.

    Google Scholar 

  9. Debray S.K., Unfold/fold transformations and loop optimisation of Logic Programs, in Proc. SIGPLAN'88 Conf. on Programming Language Disign and Implementation, SIGPLAN Notices, Vol.23, No.7, July 1988, pp. 297–307.

    Google Scholar 

  10. DeJong G., Some thoughts on the present and future of explanation-based learning, in Proc. European Conf. on Artificial Intelligence, (ECAI88), 1988, pp. 690–698.

    Google Scholar 

  11. DeJong G., Mooney R., Explanation-based learning: an alternative view, Machine Learning, Vol.1, No.2, 1986, pp. 145–176.

    Google Scholar 

  12. De Schreye D., Bruynooghe M, On the transformation of logic programs with instantiation based computation rules, J.Symbolic Computation, 1989, pp:125–154.

    Google Scholar 

  13. De Schreye D., Bruynooghe M., An application of abstract interpretation in source level program transformation, in Programming Language Implementation and Logic Programming, Deransart, Lorho, Maluszynski, eds., LNCS 348, Springer-Verlag, 1989, pp. 35–58.

    Google Scholar 

  14. Feather M.S., A system for assisting program transformation, ACM Trans. Prog. Lang. and Systems 4, 1, Jan. 1982, pp. 1–20.

    Google Scholar 

  15. Gregory S., Towards the compilation of annotated logic programs, Res.Report DOC80/16, June 1980, Imperial College.

    Google Scholar 

  16. Kedar-Cabelli S.T., McCarthy L.T., Explanation based generalization as resolution theorem proving, in Proc. of the 4th International Workshop on Machine Learning, Irvine, Morgan Kaufmann, 1987, pp. 383–389.

    Google Scholar 

  17. Komorowski H.J., A specification of an abstract Prolog machine and its applications to partial evaluation, Linkoping Studies in Science and Technology, Dissertation No.69, Linkoping University, 1981.

    Google Scholar 

  18. Kowalski R.A., Logic for problem solving, North-Holland, 1979.

    Google Scholar 

  19. Mitchell T.M., Keller R.M., Kedar-Cabelli S.T., Explanation-based generalization: a unifying view, Machine Learning, Vol.1, No.1, 1986, pp. 47–80.

    Google Scholar 

  20. Proietti M., Pettorossi A., Some strategies for transforming logic programs, report Istituto di Analisi dei Sistemi ed Informatica, Rome, 1988.

    Google Scholar 

  21. Sablon G., De Raedt L., Bruynooghe M., Generalizing multiple examples in explanation-based learning, in Proc. of International Workshop on Analogical and Inductive Inference, (AII89), LNCS, Springer-Verlag, to appear.

    Google Scholar 

  22. Sato T., Tamaki H., Transformational logic program synthesis, FGCS '84, Tokyo, 1984.

    Google Scholar 

  23. Shavlik J., DeJong G., BAGGER: an EBL system that extends and generalizes explanations, in Proc. of the Sixth National Conference on Artificial Intelligence, 1987, pp. 516–520.

    Google Scholar 

  24. Shavlik J., DeJong G., An explanation-based approach to generalizing number, in Proc. of the tenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, Milano, 1987, pp. 236–238.

    Google Scholar 

  25. Tamaki H., Sato T., Unfold/fold transformations of logic programs, in Proc. Second International Conference on Logic Programming, 1984, pp. 127–138.

    Google Scholar 

  26. Van Harmelen F., Bundy A., Explanation Based Generalization = Partial Evaluation, Artificial Intelligence, Vol.36, No.3, 1988, pp. 401–412.

    Google Scholar 

  27. Venken R., A Prolog Meta-interpreter for partial evaluation and its applications to source to source transformation and query-optimisation, in Proc. of the 6th. ECAI, 1984, pp.:91–100.

    Google Scholar 

  28. Wadler P., Listlessness is better than laziness, lazy evaluation and garbage collection at compile-time, in Proc.ACM Symp. on Lisp and Functional Programming, 1984, pp. 45–52.

    Google Scholar 

  29. Wadler P., Listlessness is better than laziness II: composing listless functions, in Proc. Conf. on Programs as data objects, Gansinger and Jones, eds., LNCS, Springer-Verlag, 1985.

    Google Scholar 

  30. Wadler P., Deforestation: transforming programs to eliminate trees, in Proc. Second European Symposium on Programming (ESOP88), ed. Gansinger, LNCS, Springer-Verlag, 1988, pp. 344–358.

    Google Scholar 

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Klaus P. Jantke

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Bruynooghe, M., De Schreye, D. (1989). Some thoughts on the role of examples in program transformation and its relevance for explanation-based learning. In: Jantke, K.P. (eds) Analogical and Inductive Inference. AII 1989. Lecture Notes in Computer Science, vol 397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-51734-0_52

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  • DOI: https://doi.org/10.1007/3-540-51734-0_52

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