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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.

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

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.

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.

The publishers of International Journal of Crowd Science wish to retract the article “Quality-time-complexity universal intelligence measurement”, by Wen Ji, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, and Yiqiang Chen, which appeared in volume 2, issue 1, 2018. It has come to our attention that due to an error in the submission process the above article is an earlier version of the article published in International Journal of Crowd Science, volume 2, issue 2, 2018 (DOI: https://doi.org/10.1108/IJCS-04-2018-0007). The duplicate publication was the result of an inadvertent administrative error by the authors. The authors and publishers of the journal sincerely apologise to the readers.