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When Almost Is Not Even Close: Remarks on the Approximability of HDTP

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Artificial General Intelligence (AGI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7999))

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Abstract

A growing number of researchers in Cognitive Science advocate the thesis that human cognitive capacities are constrained by computational tractability. If right, this thesis also can be expected to have far-reaching consequences for work in Artificial General Intelligence: Models and systems considered as basis for the development of general cognitive architectures with human-like performance would also have to comply with tractability constraints, making in-depth complexity theoretic analysis a necessary and important part of the standard research and development cycle already from a rather early stage. In this paper we present an application case study for such an analysis based on results from a parametrized complexity and approximation theoretic analysis of the Heuristic Driven Theory Projection (HDTP) analogy-making framework.

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Author information

Authors and Affiliations

  1. Institute of Cognitive Science, University of Osnabrück, Germany

    Tarek Richard Besold

  2. Department of Computer Science, University of Toronto, Canada

    Robert Robere

Authors
  1. Tarek Richard Besold
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  2. Robert Robere
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Editor information

Editors and Affiliations

  1. University of Osnabrück, Albrechtstr. 28, Germany

    Kai-Uwe Kühnberger

  2. Technische Universität Dresden, 01069, Dresden, Germany

    Sebastian Rudolph

  3. Department of Computer and Information Sciences, Temple University, Philadelphia, USA

    Pei Wang

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Besold, T.R., Robere, R. (2013). When Almost Is Not Even Close: Remarks on the Approximability of HDTP. In: Kühnberger, KU., Rudolph, S., Wang, P. (eds) Artificial General Intelligence. AGI 2013. Lecture Notes in Computer Science(), vol 7999. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39521-5_2

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  • DOI: https://doi.org/10.1007/978-3-642-39521-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39520-8

  • Online ISBN: 978-3-642-39521-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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