Bounded kolmogorov complexity based on cognitive models

Authors:
Claes Strannegård;Abdul Rahim Nizamani;Anders Sjöberg;Fredrik Engström
Affiliations:
Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Sweden,Department of Applied Information Technology, Chalmers University of Technology, Sweden;Department of Applied Information Technology, University of Gothenburg, Sweden;Department of Psychology, University of Stockholm, Sweden;Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Sweden
Venue:
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
Year:
2013

Citing 5
Cited 0

GFUN: a Maple package for the manipulation of generating and holonomic functions in one variable

ACM Transactions on Mathematical Software (TOMS)
Beyond the Turing Test

Journal of Logic, Language and Information
Sequence Learning - Paradigms, Algorithms, and Applications

Sequence Learning - Paradigms, Algorithms, and Applications
An Introduction to Kolmogorov Complexity and Its Applications

An Introduction to Kolmogorov Complexity and Its Applications
Semi-analytic natural number series induction

KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.