Journal Home > Volume 2 , Issue 1
International Journal of Crowd Science 2018, 2(1): 18-26 https://doi.org/10.1108/IJCS-01-2018-0003
Research paper | Open Access | Issue | Published: 05 June 2018

Quality-time-complexity universal intelligence measurement

Show Author's Information Wen Ji( ),  Jing Liu,  Zhiwen Pan,  Jingce Xu,  Bing Liang,  Yiqiang Chen
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Keywords:
Turing test, Agent-environment framework, Algorithmic information theory, Kolmogorov complexity, Universal intelligence
Cite this article:
Ji W, Liu J, Pan Z, et al. Quality-time-complexity universal intelligence measurement. International Journal of Crowd Science, 2018, 2(1): 18-26. https://doi.org/10.1108/IJCS-01-2018-0003
Download citation
EndNote(RIS)
BibTeX

519

Views

9

Downloads

Citations

1

Crossref

N/A

WoS

1

Scopus

N/A

CSCD

Abstract Full text About this article
Purpose

With development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more and more autonomous and smart. Therefore, there is a growing demand to develop a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.

Design/methodology/approach

This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.

Findings

By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.

Practical implications

In a crowd network, a number of intelligent agents are able to collaborate with each other to finish a certain kind of sophisticated tasks. The proposed approach can be used to allocate the tasks to the agents within a crowd network in an optimized manner.

Originality/value

This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.


menu
Abstract
Full text
Outline
About this article

Quality-time-complexity universal intelligence measurement

Show Author's information Wen Ji( ), Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, Yiqiang Chen
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Abstract

Purpose

With development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more and more autonomous and smart. Therefore, there is a growing demand to develop a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.

Design/methodology/approach

This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.

Findings

By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.

Practical implications

In a crowd network, a number of intelligent agents are able to collaborate with each other to finish a certain kind of sophisticated tasks. The proposed approach can be used to allocate the tasks to the agents within a crowd network in an optimized manner.

Originality/value

This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.

Keywords: Turing test, Agent-environment framework, Algorithmic information theory, Kolmogorov complexity, Universal intelligence

References(15)

Alvarado, N., Adams, S.S., Burbeck, S. and Latta, C. (2001), “Project Joshua blue: design considerations for evolving an emotional mind in a simulated environment”, Proceedings of the 2001 AAAI Fall Symposium Emotional and Intelligent II: The Tangled Knot of Social Cognition.

Gibson, E. (1998), “Linguistic complexity: locality of syntactic dependencies”, Cognition, Vol. 68 No. 1, pp. 1-76.
DOI Google Scholar
Hernndez-Orallo, J. (2014), “AI evaluation: past, present and future”, arXiv preprint arXiv:1408.6908.
Legg, S. and Hutter, M. (2006), “A formal measure of machine intelligence”, arXiv preprint cs/0605024.
Legg, S. and Veness, J. (2013), “An approximation of the universal intelligence measure”, Algorithmic Probability and Friends: Bayesian Prediction and Artificial Intelligence, Springer, Berlin Heidelberg, pp. 236-249.https://doi.org/10.1007/978-3-642-44958-1_18
DOI

Levin, L.A. (1973), “Universal sequential search problems”, Problemy Peredachi Informatsii, Vol. 9, pp. 115-116.
Google Scholar
Li, M. and Paul, M.B. (2008), An Introduction to Kolmogorov Complexity and Its Applications, Springer-Verlag, New York, NY, p. 3.
Longo, G. (2009), “Turing and the imitation game impossible geometry”, Parsing the Turing Test, Springer, Berlin, pp. 377-411.https://doi.org/10.1007/978-1-4020-6710-5_23
DOI
Mahoney, M.V. (1999), “Text compression as a test for artificial intelligence”, AAAI/IAAI: 970.
Masum, H., Christensen, S. and Oppacher, F. (2002), “The turing ratio: metrics for open-ended tasks”, Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, Morgan Kaufmann Publishers, pp. 973-980.
Oppy, G. and Dowe, D. (2003), available at: https://seop.illc.uva.nl/entries/turing-test/
Prpic, J. and Shukla, P. (2016), “Crowd science: measurements, models, and methods”, 49th Hawaii International Conference on IEEE System Sciences (HICSS), 2016, pp. 4365-4374.https://doi.org/10.1109/HICSS.2016.542
DOI
Smith, W.D. (2006), “Mathematical definition of intelligence (and consequences)”, Unpublished report.
Solomonoff, R.J. (2009), “Algorithmic probability: theory and applications”, Information Theory and Statistical Learning, Springer, New York, NY, pp. 1-23.https://doi.org/10.1007/978-0-387-84816-7_1
DOI

Turing, A.M. (1950), “Computing machinery and intelligence”, Mind, Vol. 59 No. 236, pp. 433-460.
DOI Google Scholar
Publication history
Copyright
Rights and permissions

Publication history

Received: 26 January 2018
Revised: 16 March 2018
Accepted: 02 April 2018
Published: 05 June 2018
Issue date: July 2018

Copyright

©2018 International Journal of Crowd Science

Rights and permissions

Wen Ji, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang and Yiqiang Chen. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Return