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

  • Tarek Richard Besold
  • Robert Robere
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7999)

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.

Keywords

Function Symbol General Intelligence Theory Projection Admissible Sequence Term Algebra 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tarek Richard Besold
    • 1
  • Robert Robere
    • 2
  1. 1.Institute of Cognitive ScienceUniversity of OsnabrückGermany
  2. 2.Department of Computer ScienceUniversity of TorontoCanada

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