Bounded Kolmogorov Complexity Based on Cognitive Models

  • Claes Strannegård
  • Abdul Rahim Nizamani
  • Anders Sjöberg
  • Fredrik Engström
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7999)

Abstract

Computable versions of Kolmogorov complexity have been used in the context of pattern discovery [1]. However, these complexity measures do not take the psychological dimension of pattern discovery into account. We propose a method for pattern discovery based on a version of Kolmogorov complexity where computations are restricted to a cognitive model with limited computational resources. The potential of this method is illustrated by implementing it in a system used to solve number sequence problems. The system was tested on the number sequence problems of the IST IQ test [2], and it scored 28 out of 38 problems, above average human performance, whereas the mathematical software packages Maple, Mathematica, and WolframAlpha scored 9, 9, and 12, respectively. The results obtained and the generalizability of the method suggest that this version of Kolmogorov complexity is a useful tool for pattern discovery in the context of AGI.

Keywords

artificial general intelligence cognitive model Kolmogorov complexity pattern discovery 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Claes Strannegård
    • 1
    • 2
  • Abdul Rahim Nizamani
    • 3
  • Anders Sjöberg
    • 4
  • Fredrik Engström
    • 1
  1. 1.Department of Philosophy, Linguistics and Theory of ScienceUniversity of GothenburgSweden
  2. 2.Department of Applied Information TechnologyChalmers University of TechnologySweden
  3. 3.Department of Applied Information TechnologyUniversity of GothenburgSweden
  4. 4.Department of PsychologyUniversity of StockholmSweden

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