Advertisement

How Does Predicate Invention Affect Human Comprehensibility?

  • Ute Schmid
  • Christina Zeller
  • Tarek Besold
  • Alireza Tamaddoni-Nezhad
  • Stephen Muggleton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10326)

Abstract

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as that of Mitchell, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present the results of experiments testing human comprehensibility of logic programs learned with and without predicate invention. Results indicate that comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols.

References

  1. 1.
    Cropper, A., Muggleton, S.H.: Learning efficient logical robot strategies involving composable objects. In: Proceedings of the 24th International Joint Conference Artificial Intelligence (IJCAI 2015), pp. 3423–3429 (2015)Google Scholar
  2. 2.
    Cropper, A., Muggleton, S.H.: Learning higher-order logic programs through abstraction and invention. In: Proceedings of the 25th International Joint Conference Artificial Intelligence (IJCAI 2016), pp. 1418–1424 (2016)Google Scholar
  3. 3.
    Forbus, K.D.: Software social organisms: implications for measuring AI progress. AI Mag. 37(1), 85–90 (2016)Google Scholar
  4. 4.
    Freitas, A.A.: Comprehensible classification models: a position paper. SIGKDD Explor. Newsl. 15(1), 1–10 (2014)CrossRefGoogle Scholar
  5. 5.
    Kahney, H.: What do novice programmers know about recursion? In: Soloway, E., Spohrer, J.C. (eds.) Studying the Novice Programmer, pp. 209–228. Lawrence Erlbaum (1989)Google Scholar
  6. 6.
    Letham, B., Rudin, C., McCormick, T.H., Madigan, D.: Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9(3), 1350–1371 (2015)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Lin, D., Dechter, E., Ellis, K., Tenenbaum, J.B., Muggleton, S.H.: Bias reformulation for one-shot function induction. In: Proceedings of the 23rd European Conference on Artificial Intelligence (ECAI 2014), pp. 525–530. IOS Press (2014)Google Scholar
  8. 8.
    Michie, D.: Machine learning in the next five years. In: Proceedings of the Third European Working Session on Learning, pp. 107–122. Pitman (1988)Google Scholar
  9. 9.
    Mozina, M., Zabkar, J., Bratko, I.: Argument based machine learning. Artif. Intell. 171(10–15), 922–937 (2007)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Muggleton, S.H., Buntine, W.: Machine invention of first-order predicates by inverting resolution. In: Proceedings of the 5th International Conference on Machine Learning, pp. 339–352. Kaufmann (1988)Google Scholar
  11. 11.
    Muggleton, S.H., Lin, D., Pahlavi, N., Tamaddoni-Nezhad, A.: Meta-interpretive learning: application to grammatical inference. Mach. Learn. 94, 25–49 (2014)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Muggleton, S.H., Lin, D., Tamaddoni-Nezhad, A.: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach. Learn. 100(1), 49–73 (2015)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Muggleton, S.H., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K.: ILP turns 20: biography and future challenges. Mach. Learn. 86(1), 3–23 (2011)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5, 239–266 (1990)Google Scholar
  15. 15.
    Rouveirol, C., Puget, J.-F.: A simple and general solution for inverting resolution. In: Proceedings of the fourth European Working Session on Learning (EWSL-1989), pp. 201–210. Pitman (1989)Google Scholar
  16. 16.
    Srinivasan, A.: The ALEPH manual. Machine Learning at the Computing Laboratory, Oxford University (2001)Google Scholar
  17. 17.
    Stahl, I.: Constructive induction in inductive logic programming: an overview. Technical report, Fakultät Informatik, Universität Stuttgart (1992)Google Scholar
  18. 18.
    Sterling, L., Shapiro, E.Y.: The Art of Prolog: Advanced Programming Techniques. MIT Press, Cambridge (1994)MATHGoogle Scholar
  19. 19.
    Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ute Schmid
    • 1
  • Christina Zeller
    • 1
  • Tarek Besold
    • 2
  • Alireza Tamaddoni-Nezhad
    • 3
  • Stephen Muggleton
    • 3
  1. 1.University of BambergBambergGermany
  2. 2.University of BremenBremenGermany
  3. 3.Imperial College LondonLondonUK

Personalised recommendations