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Research paper | Open Access

Quality-time-complexity universal intelligence measurement

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
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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 testAgent-environment frameworkAlgorithmic information theoryKolmogorov complexityUniversal intelligence

References

 
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International Journal of Crowd Science
Volume 2 Issue 1,
July 2018
Pages 18-26
DOI: 10.1108/IJCS-01-2018-0003
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

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Received: 26 January 2018
Revised: 16 March 2018
Accepted: 02 April 2018
Published: 05 June 2018
©2018 International Journal of Crowd Science

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
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