International Journal of Crowd Science 2018, 2(2): 99-107 https://doi.org/10.1108/IJCS-04-2018-0007

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Open Access | Issue | Published: 16 October 2018

Quality-time-complexity universal intelligence measurement

Show Author's Information Hide Author's Information Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, Yiqiang Chen, Wen Ji(

)

Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Keywords:

Turing test, Algorithmic information theory, Kolmogorov complexity, Universal intelligence, Agent–environment framework

Cite this article:

Liu J, Pan Z, Xu J, et al. Quality-time-complexity universal intelligence measurement. International Journal of Crowd Science, 2018, 2(2): 99-107. https://doi.org/10.1108/IJCS-04-2018-0007

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Abstract Full text About this article

Abstract

Purpose

With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing 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.

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.

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About this article

Quality-time-complexity universal intelligence measurement

Show Author's information Hide Author's Information Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, Yiqiang Chen, Wen Ji(

)

Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

Abstract

Purpose

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.

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, Algorithmic information theory, Kolmogorov complexity, Universal intelligence, Agent–environment framework

References(13)

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Publication history

Acknowledgements

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Publication history

Received: 17 April 2018

Revised: 15 June 2018

Accepted: 21 June 2018

Published: 16 October 2018

Issue date: November 2018

Copyright

Acknowledgements

This work is supported by the National Key Research & Development Plan of China (2017YFB1400100), the National Natural Science Foundation of China (61572466), and the Beijing Natural Science Foundation (4162059).

Rights and permissions

Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, Yiqiang Chen and Wen Ji. 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