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Bounded Kolmogorov Complexity Based on Cognitive Models

Journal article
Authors Claes Strannegård
Abdul Rahim Nizamani
Sjöberg Anders
Fredrik Engström
Published in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volume 7999
Pages 130-139
ISSN 1867-8211
Publication year 2013
Published at Department of Applied Information Technology (GU)
Department of Philosophy, Linguistics and Theory of Science
Pages 130-139
Language en
Links dx.doi.org/10.1007/978-3-642-39521-...
https://gup.ub.gu.se/file/204378
Keywords artificial general intelligence, cognitive model, Kolmogorov complexity, pattern discovery.
Subject categories Logic

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.

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