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International Conference on Artificial General Intelligence

AGI 2015: Artificial General Intelligence pp 352–361Cite as

Comparing Computer Models Solving Number Series Problems

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9205))

Abstract

Inductive reasoning requires to find for given instances a general rule. This makes inductive reasoning an excellent test-bed for artificial general intelligence (AGI). An example being part of many IQ-tests are number series: for a given sequence of numbers the task is to find a next “correct” successor number. Successful reasoning may require to identify regular patterns and to form a rule, an implicit underlying function that generates this number series. Number series problems can be designed along different dimensions, such as structural complexity, required mathematical background knowledge, and even insights based on a perspective switch. The aim of this paper is to give an overview of existing cognitive and computational models, their underlying algorithmic approaches and problem classes. A first empirical comparison of some of these approaches with focus on artificial neural nets and inductive programming is presented.

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Author information

Authors and Affiliations

  1. Cognitive Systems Group, University of Bamberg, Bamberg, Germany

    Ute Schmid

  2. Foundations of AI, Technical Faculty, University of Freiburg, Freiburg im Breisgau, Germany

    Marco Ragni

Authors
  1. Ute Schmid
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  2. Marco Ragni
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Correspondence to Ute Schmid .

Editor information

Editors and Affiliations

  1. Reykjavik University, Reykjavik, Iceland

    Jordi Bieger

  2. Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR

    Ben Goertzel

  3. Mechanics and Optics, Saint Petersburg State University of Information Technologies, St. Petersburg, Russia

    Alexey Potapov

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Schmid, U., Ragni, M. (2015). Comparing Computer Models Solving Number Series Problems. In: Bieger, J., Goertzel, B., Potapov, A. (eds) Artificial General Intelligence. AGI 2015. Lecture Notes in Computer Science(), vol 9205. Springer, Cham. https://doi.org/10.1007/978-3-319-21365-1_36

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  • DOI: https://doi.org/10.1007/978-3-319-21365-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21364-4

  • Online ISBN: 978-3-319-21365-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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