Quality-time-complexity universal intelligence measurement
Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, Yiqiang Chen, Wen Ji · International Journal of Crowd Science · 2018
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.