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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Please note that the relation mother(matilda,bill) needs to be changed to mother(matilda,alice) and the relation father(jake,bill) needs to be changed to father(jake,alice) to cover all cases necessary to invent the predicate parent/2 in the context of different target predicates.
- 2.
A comprehensive description of all analyses and results can be found at http://www.cogsys.wiai.uni-bamberg.de/publications/comprAnalysesDoc.pdf.
References
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)
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)
Forbus, K.D.: Software social organisms: implications for measuring AI progress. AI Mag. 37(1), 85–90 (2016)
Freitas, A.A.: Comprehensible classification models: a position paper. SIGKDD Explor. Newsl. 15(1), 1–10 (2014)
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)
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)
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)
Michie, D.: Machine learning in the next five years. In: Proceedings of the Third European Working Session on Learning, pp. 107–122. Pitman (1988)
Mozina, M., Zabkar, J., Bratko, I.: Argument based machine learning. Artif. Intell. 171(10–15), 922–937 (2007)
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)
Muggleton, S.H., Lin, D., Pahlavi, N., Tamaddoni-Nezhad, A.: Meta-interpretive learning: application to grammatical inference. Mach. Learn. 94, 25–49 (2014)
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)
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)
Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5, 239–266 (1990)
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)
Srinivasan, A.: The ALEPH manual. Machine Learning at the Computing Laboratory, Oxford University (2001)
Stahl, I.: Constructive induction in inductive logic programming: an overview. Technical report, Fakultät Informatik, Universität Stuttgart (1992)
Sterling, L., Shapiro, E.Y.: The Art of Prolog: Advanced Programming Techniques. MIT Press, Cambridge (1994)
Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Schmid, U., Zeller, C., Besold, T., Tamaddoni-Nezhad, A., Muggleton, S. (2017). How Does Predicate Invention Affect Human Comprehensibility?. In: Cussens, J., Russo, A. (eds) Inductive Logic Programming. ILP 2016. Lecture Notes in Computer Science(), vol 10326. Springer, Cham. https://doi.org/10.1007/978-3-319-63342-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-63342-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63341-1
Online ISBN: 978-3-319-63342-8
eBook Packages: Computer ScienceComputer Science (R0)