Skip to main content

Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol

  • Conference paper
  • First Online:
  • 755 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11105))

Abstract

Many people believe that every fourth year is a leap year. However, this rule is too general: year X is a leap year if X is divisible by 4 except if X is divisible by 100 except if X is divisible by 400. We call such a theory with alternating generalisation and specialisation a step-wise narrowed theory. We present and evaluate an extension to the ILP system Metagol which facilitates learning such theories. We enabled Metagol to learn over-general theories by allowing a limited number of false positives during learning. This variant is iteratively applied on a learning task. For each iteration after the first, positive examples are the false positives from the previous iteration and negative examples are the true positives from the previous iteration. Iteration continues until no more false positives are present. Then, the theories are combined to a single step-wise narrowed theory. We evaluate the usefulness of our approach in the leap year domain. We can show that our approach finds solutions with fewer clauses, higher accuracy, and in shorter time.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Of course, predicates with a “negative” semantic, like not_father/2, can be supplied in the background knowledge. Nevertheless, no syntactic negation can be induced.

  2. 2.

    Nevertheless, Metagol can only abduce clauses for the predicate symbol of the first positive example and invented predicate symbols derived thereof.

  3. 3.

    We forked from commit 1524600225a65237de9578e46127049f6f95d1a4 in the GitHub Metagol repository [14].

  4. 4.

    Metagol\(_{SN}\) is available at https://github.com/michael-siebers/metagol/tree/ilp2018.

References

  1. Richards, E.G.: Calendars. In: Urban, S.E., Seidelmann, P.K. (eds.) Explanatory Supplement to the Astronomical Almanac, pp. 585–624 (2013)

    Google Scholar 

  2. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  3. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  4. Muggleton, S., Buntine, W.: Machine invention of first-order predicates by inverting resolution. In: Machine Learning Proceeding, pp. 339–352 (1988), https://doi.org/10.1016/B978-0-934613-64-4.50040-2

  5. Bain, M., Muggleton, S.: Non-monotonic Learning. In: Machine Intelligence 12 - Towards an Automated Logic of Human Thought, pp. 105–120 (1991)

    Google Scholar 

  6. Malerba, D., Esposito, F., Lisi, F.A.: Learning Recursive Theories with ATRE. In: ECAI (European Conference on Artificial Intelligence), pp. 435–439 (1998)

    Google Scholar 

  7. Malerba, D.: Learning recursive theories in the normal ILP setting. Fundamenta Informaticae 57, 39–77 (2003)

    Google Scholar 

  8. Ray, O.: Nonmonotonic abductive inductive learning. J. Appl. Logic 7, 329–340 (2009)

    Article  MathSciNet  Google Scholar 

  9. Stahl, I.: Predicate Invention in ILP - an Overview. In: Machine Learning: ECML-1993, pp. 313–322 (1993)

    Google Scholar 

  10. Muggleton, S.H., Lin, D., Pahlavi, N., Tamaddoni-Nezhad, A.: Meta-interpretive learning: application to grammatical inference. Mach. Learn. 94, 25–49 (2014)

    Article  MathSciNet  Google Scholar 

  11. Lin, D., Dechter, E., Ellis, K., Tenenbaum, J., Muggleton, S.H.: Bias reformulation for one-shot function induction. In: ECAI (European Conference on Artificial Intelligence), pp. 525–530 (2014). https://doi.org/10.3233/978-1-61499-419-0-525

  12. Cropper, A., Muggleton, S.H.: Learning higher-order logic programs through abstraction and invention. In: IJCAI (International Joint Conference on Artificial Intelligence), pp. 1418–1424 (2016)

    Google Scholar 

  13. Muggleton, S.H., Lin, D., Tamaddoni-Nezhad, A.: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach. Learn. 100, 49–73 (2015)

    Article  MathSciNet  Google Scholar 

  14. Cropper, A., Muggleton, S.H.: Metagol System (2016). https://github.com/metagol/metagol

  15. Larson, J., Michalski, R.S.: Inductive Inference of VL Decision Rules. ACM SIGART Bull., 38–44, June 1977

    Google Scholar 

  16. Siebers, M., Schmid, U., Seuß, D., Kunz, M., Lautenbacher, S.: Characterizing facial expressions by grammars of action unit sequences - a first investigation using ABL. Inf. Sci. 329, 866–875 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

We like to thank Andrew Cropper for valuable discussions on negation in Metagol. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SCHM 1239/10-1.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Siebers .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Siebers, M., Schmid, U. (2018). Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol. In: Riguzzi, F., Bellodi, E., Zese, R. (eds) Inductive Logic Programming. ILP 2018. Lecture Notes in Computer Science(), vol 11105. Springer, Cham. https://doi.org/10.1007/978-3-319-99960-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99960-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99959-3

  • Online ISBN: 978-3-319-99960-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics