Quality-time-complexity universal intelligence measurement

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

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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.

Keywords

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

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International Journal of Crowd Science

Volume 2 Issue 2,
November 2018

Pages 99-107

DOI: 10.1108/IJCS-04-2018-0007

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