Skip to main content
Springer Nature Link
Log in

Semi-analytic Natural Number Series Induction

  • Conference paper
KI 2012: Advances in Artificial Intelligence (KI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7526))

Included in the following conference series:

Abstract

The induction of natural number series is a prototypical intelligence test task. We present a system which solves this task semi-analytically. As first step the term structure defining a given number series is guessed. Then the semi-instantiated formula is used to abduct new number series examples which can be solved more easily.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Burghardt, J.: E-generalization using grammars. Artificial Intelligence 165, 1–35 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  • Lovett, A., Forbus, K., Usher, J.: A structure-mapping model of Raven’s Progressive Matrices. In: Proceedings of CogSci 2010 (2010)

    Google Scholar 

  • Ragni, M., Klein, A.: Predicting Numbers: An AI Approach to Solving Number Series. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS, vol. 7006, pp. 255–259. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  • Schmid, U., Kitzelmann, E.: Inductive rule learning on the knowledge level. Cognitive Systems Research 12(3), 237–248 (2011)

    Article  Google Scholar 

  • Tenenbaum, J., Griffiths, T., Kemp, C.: Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 10(7), 309–318 (2006)

    Article  Google Scholar 

  • The Online Encyclopedia of Integer Sequences (2012), http://oeis.org/

Download references

Author information

Authors and Affiliations

  1. Cognitive Systems Group, Faculty Information Systems and Applied Computer Science, University of Bamberg, Germany

    Michael Siebers & Ute Schmid

Authors
  1. Michael Siebers
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ute Schmid
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Artificial Intelligence, University of Ulm, 89069, Ulm, Germany

    Birte Glimm

  2. Saarland University and German Research Center for Artificial Intelligence (DFKI), Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Antonio Krüger

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Siebers, M., Schmid, U. (2012). Semi-analytic Natural Number Series Induction. In: Glimm, B., Krüger, A. (eds) KI 2012: Advances in Artificial Intelligence. KI 2012. Lecture Notes in Computer Science(), vol 7526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33347-7_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33347-7_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33346-0

  • Online ISBN: 978-3-642-33347-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation