Quality-time-complexity universal intelligence measurement

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.