Advertisement

Inductive Programming: A Survey of Program Synthesis Techniques

  • Emanuel Kitzelmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5812)

Abstract

Inductive programming (IP)—the use of inductive reasoning methods for programming, algorithm design, and software development—is a currently emerging research field. A major subfield is inductive program synthesis, the (semi-)automatic construction of programs from exemplary behavior. Inductive program synthesis is not a unified research field until today but scattered over several different established research fields such as machine learning, inductive logic programming, genetic programming, and functional programming. This impedes an exchange of theory and techniques and, as a consequence, a progress of inductive programming. In this paper we survey theoretical results and methods of inductive program synthesis that have been developed in different research fields until today.

Keywords

Logic Program Inductive Logic Programming Horn Clause Recursive Program Program Synthesis 
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.
    Partridge, D.: The case for inductive programming. Computer 30(1), 36–41 (1997)CrossRefGoogle Scholar
  2. 2.
    Flener, P., Partridge, D.: Inductive programming. Automated Software Engineering 8(2), 131–137 (2001)CrossRefGoogle Scholar
  3. 3.
    Schmid, U.: Inductive Synthesis of Functional Programs: Universal Planning, Folding of Finite Programs, and Schema Abstraction by Analogical Reasoning. LNCS (LNAI), vol. 2654. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  4. 4.
    Summers, P.D.: A methodology for LISP program construction from examples. Journal of the ACM 24(1), 161–175 (1977)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  6. 6.
    McCarthy, J.: Recursive functions of symbolic expressions and their computation by machine, part i. Communications of the ACM 3(4), 184–195 (1960)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Smith, D.R.: The synthesis of LISP programs from examples: A survey. In: Biermann, A., Guiho, G., Kodratoff, Y. (eds.) Automatic Program Construction Techniques, pp. 307–324. Macmillan, Basingstoke (1984)Google Scholar
  8. 8.
    Jouannaud, J.P., Kodratoff, Y.: Program synthesis from examples of behavior. In: Biermann, A.W., Guiho, G. (eds.) Computer Program Synthesis Methodologies, pp. 213–250. D. Reidel Publ. Co. (1983)Google Scholar
  9. 9.
    Plotkin, G.D.: A note on inductive generalization. Machine Intelligence 5, 153–163 (1970)MathSciNetGoogle Scholar
  10. 10.
    Biermann, A.W.: The inference of regular LISP programs from examples. IEEE Transactions on Systems, Man and Cybernetics 8(8), 585–600 (1978)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Kitzelmann, E., Schmid, U.: Inductive synthesis of functional programs: An explanation based generalization approach. Journal of Machine Learning Research 7, 429–454 (2006)MathSciNetGoogle Scholar
  12. 12.
    Kitzelmann, E.: Analytical inductive functional programming. In: Hanus, M. (ed.) Logic-Based Program Synthesis and Transformation. LNCS, vol. 5438, pp. 87–102. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Muggleton, S.H., De Raedt, L.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19, 20, 629–679 (1994)CrossRefGoogle Scholar
  14. 14.
    Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)Google Scholar
  15. 15.
    Shapiro, E.Y.: Algorithmic Program Debugging. MIT Press, Cambridge (1983)Google Scholar
  16. 16.
    Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A midterm report. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 3–20. Springer, Heidelberg (1993)Google Scholar
  17. 17.
    Muggleton, S.H., Feng, C.: Efficient induction of logic programs. In: Proceedings of the First Conference on Algorithmic Learning Theory, Ohmsha, pp. 368–381 (1990)Google Scholar
  18. 18.
    Muggleton, S.H.: Inverse entailment and progol. New Generation Computing 13, 245–286 (1995)CrossRefGoogle Scholar
  19. 19.
    Flener, P., Yilmaz, S.: Inductive synthesis of recursive logic programs: Achievements and prospects. The Journal of Logic Programming 41(2-3), 141–195 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Aha, D.W., Lapointe, S., Ling, C.X., Matwin, S.: Inverting implication with small training sets. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 29–48. Springer, Heidelberg (1994)Google Scholar
  21. 21.
    Rios, R., Matwin, S.: Efficient induction of recursive prolog definitions. In: McCalla, G.I. (ed.) Canadian AI 1996. LNCS, vol. 1081, pp. 240–248. Springer, Heidelberg (1996)Google Scholar
  22. 22.
    Idestam-Almquist, P.: Efficient induction of recursive definitions by structural analysis of saturations. In: Advances in Inductive Logic Programming. IOS Press, Amsterdam (1996)Google Scholar
  23. 23.
    Furusawa, M., Inuzuka, N., Seki, H., Itoh, H.: Induction of logic programs with more than one recursive clause by analyzing saturations. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 165–172. Springer, Heidelberg (1997)Google Scholar
  24. 24.
    Mofizur, C.R., Numao, M.: Top-down induction of recursive programs from small number of sparse examples. In: Advances in Inductive Logic Programming. IOS Press, Amsterdam (1996)Google Scholar
  25. 25.
    Bergadano, F., Gunetti, D.: Inductive Logic Programming: From Machine Learning to Software Engineering. MIT Press, Cambridge (1995)Google Scholar
  26. 26.
    Flener, P.: Inductive logic program synthesis with DIALOGS. In: ILP 1996. LNCS, vol. 1314, pp. 175–198. Springer, Heidelberg (1997)Google Scholar
  27. 27.
    Jorge, A.M.G.: Iterative Induction of Logic Programs. PhD thesis, Departamento de Ciência de Computadores, Universidade do Porto (1998)Google Scholar
  28. 28.
    Ferri-Ramírez, C., Hernández-Orallo, J., Ramírez-Quintana, M.: Incremental learning of functional logic programs. In: Kuchen, H., Ueda, K. (eds.) FLOPS 2001. LNCS, vol. 2024, pp. 233–247. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  29. 29.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  30. 30.
    Koza, J.R., Andre, D., Bennett, F.H., Keane, M.A.: Genetic Programming III: Darwinian Invention & Problem Solving. Morgan Kaufmann, San Francisco (1999)zbMATHGoogle Scholar
  31. 31.
    Wong, M., Mun, T.: Evolving recursive programs by using adaptive grammar based genetic programming. Genetic Programming and Evolvable Machines 6(4), 421–455 (2005)CrossRefGoogle Scholar
  32. 32.
    Yu, T.: Hierarchical processing for evolving recursive and modular programs using higher-order functions and lambda abstraction. Genetic Programming and Evolvable Machines 2(4), 345–380 (2001)zbMATHCrossRefGoogle Scholar
  33. 33.
    Kahrs, S.: Genetic programming with primitive recursion. In: Proceedings of the 8th annual Conference on Genetic and Evolutionary Computation (GECCO 2006), pp. 941–942. ACM, New York (2006)CrossRefGoogle Scholar
  34. 34.
    Binard, F., Felty, A.: Genetic programming with polymorphic types and higher-order functions. In: Proceedings of the 10th annual Conference on Genetic and Evolutionary Computation (GECCO 2008), pp. 1187–1194. ACM Press, New York (2008)CrossRefGoogle Scholar
  35. 35.
    Hamel, L., Shen, C.: An inductive programming approach to algebraic specification. In: Proceedings of the 2nd Workshop on Approaches and Applications of Inductive Programming (AAIP 2007), pp. 3–14 (2007)Google Scholar
  36. 36.
    Olsson, J.R.: Inductive functional programming using incremental program transformation. Artificial Intelligence 74(1), 55–83 (1995)CrossRefGoogle Scholar
  37. 37.
    Katayama, S.: Systematic search for lambda expressions. In: van Eekelen, M.C.J.D. (ed.) Revised Selected Papers from the Sixth Symposium on Trends in Functional Programming, TFP 2005, vol. 6, pp. 111–126. Intellect (2007)Google Scholar
  38. 38.
    Koopman, P., Alimarine, A., Tretmans, J., Plasmeijer, R.: GAST: Generic automated software testing. In: Peña, R., Arts, T. (eds.) IFL 2002. LNCS, vol. 2670. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  39. 39.
    Hofmann, M., Kitzelmann, E., Schmid, U.: A unifying framework for analysis and evaluation of inductive programming systems. In: Proceedings of the Second Conference on Artificial General Intelligence, Atlantis, pp. 55–60 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Emanuel Kitzelmann
    • 1
  1. 1.Cognitive Systems GroupUniversity of Bamberg 

Personalised recommendations