Abstract
Purpose Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution. Design/methodology/approach In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend. Findings This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution. Practical implications Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources. Originality/value In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.
Highlights
Before the concept of crowd intelligence came into being, most of the work is based on the measurement of single agent intelligence
Differential evolution and K-means clustering for the evolution of crowd intelligence To better build and improve the evolution model of crowd intelligence on the basis of existing knowledge, we present the basic characteristics of differential evolution (DE) model and Kmeans clustering
4.2 Experimental results and analysis For the crowd intelligence with “*” label in Figure 2, five simulation experiments of DE and K-means clustering are conducted with the calculation formula to calculate the evaluation index normalized mutual information (NMI) of each clustering
Summary
Before the concept of crowd intelligence came into being, most of the work is based on the measurement of single agent intelligence. The measurement of monomer intelligence can no longer meet the needs of development. The research on human crowd intelligence (Anita et al, 2010) has become the cornerstone of the measurement method of crowd intelligence. IQ testing (David and José, 2012) has been improved to measure agents. Based on the quality time complexity system, Ji et al (2018) implement the measurement method of crowd intelligence. This transcendental knowledge has inspired us to further explore crowd intelligence
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