Abstract
Purpose Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed. Design/methodology/approach In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them. Findings To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation. Originality/value Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations.
Highlights
As human beings enter the network era, big data and artificial intelligence continue to improve the intelligence of people, machines and objects
The crowd network is the idea of the intelligent agent and its consciousness space in the physical space, which is mapped to the intelligent agents in the information space and the intelligent agents form the crowd network through interconnection (Ikediego et al, 2018)
For the generality of the crowd collaboration simulation framework, we propose the concept of pattern
Summary
As human beings enter the network era, big data and artificial intelligence continue to improve the intelligence of people, machines and objects. In the crowd collaborative simulation group, we call the simulation individual as the swarm intelligence unit and set it to have two kinds of roles. Because of the existence of adviser, monitor and mutation rate, the results are not necessarily the same even when swarm intelligence units achieve the same goal, and because the number of individuals in the group is large and their abilities are different, there are many schemes of decomposition and distribution. The agents in these information spaces reflect the behaviors of agents in physical space and their respective psychological consciousness in real time, and realize accurate, timely and dynamic interconnection through network interconnection, intelligent search, interactive interaction, transaction matching and other operations (with the help of intelligent software algorithm), and generate various interactive behaviors It is characterized by personalized and active consumption, centralized and direct circulation, intelligent and decentralized production, personalized and convenient life, forming networked wisdom-based economic and social form of the internet of all things, which indicates that human beings are entering the wisdom network era of wisdom interconnection. There are many ways to accomplish the goal, and the consumption and the profit of each are different
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