@article{Ji2018, 
author = {Wen Ji and Jing Liu and Zhiwen Pan and Jingce Xu and Bing Liang and Yiqiang Chen},
title = {Quality-time-complexity universal intelligence measurement},
year = {2018},
journal = {International Journal of Crowd Science},
volume = {2},
number = {1},
pages = {18-26},
keywords = {Turing test, Agent-environment framework, Algorithmic information theory, Kolmogorov complexity, Universal intelligence},
url = {https://www.sciopen.com/article/10.1108/IJCS-01-2018-0003},
doi = {10.1108/IJCS-01-2018-0003},
abstract = {PurposeWith 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/approachThis paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.FindingsBy 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 implicationsIn 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/valueThis 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.}
}