Bounded Kolmogorov Complexity Based on Cognitive Models
Journal article, 2013

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.

Kolmogorov complexity

artificial general intelligence

cognitive model

pattern discovery.


Claes Strannegård

University of Gothenburg

Chalmers, Applied Information Technology (Chalmers), Cognition and Communication

Abdul Rahim Nizamani

University of Gothenburg

Sjöberg Anders

Stockholm University

Fredrik Engström

University of Gothenburg

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering

1867-8211 (ISSN)

Vol. 7999 130-139

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