复杂性视角下的集体智慧产生机制研究

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Bio-Inspired Computation Pub Date : 2023-01-01 DOI:10.1504/ijbic.2023.133500
Renbin Xiao, Zhenhui Feng, Bowen Wu
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引用次数: 0

摘要

本文从复杂性的角度探讨了集体智能的产生机制。本文首先根据CI的两个基本阶段,即CI 1.0 (swarm intelligence)和CI 2.0 (crowd intelligence),对CI的主要特征进行了比较。考虑到这两个阶段之间的联系机制尚不清楚,我们认为高等生物群体行为是低等生物群体行为向群体行为的过渡。因此,CI的仿生原型可以分为三类:低等生物、高等生物和人类。本文首先细化了以分工为代表的低等生物CI的出现机制,即刺激-反应机制和激活-抑制机制。在此基础上,揭示了基于角色划分和感知驱动的吸引-排斥机制。在此基础上,通过基于吸引-排斥机制的过程进化描述,分别阐述了群体智力在感知和认知层面的产生机制。最后,本研究对CI的产生机制进行了整体阐释。
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Research on emergence mechanism of collective intelligence from the complexity perspective
This paper explores the emergence mechanism of collective intelligence (CI) from the complexity perspective. It begins with a comparison of the main features based on the two basic stages of CI, i.e., CI 1.0 (swarm intelligence) and CI 2.0 (crowd intelligence). Considering the connection mechanism between the two stages is still unclear, we would regard higher organism group behaviours as the transition between lower organism group behaviours to crowd behaviours. Accordingly, the bionic prototypes of CI can be classified into three categories: lower organisms, higher organisms and humans. This paper first refined the emergence mechanisms of CI in lower organisms represented by labour division, i.e., stimulus-response mechanism and activation-inhibition mechanism. Subsequently, the higher organism emergence mechanism was revealed, which is the attraction-repulsion mechanism based on roles division and perception driven. Furthermore, the emergence mechanism of crowd intelligence at the perceptual level and cognitive level are presented respectively, by means of process evolutionary description based on the attraction-repulsion mechanism. Finally, the research gives a holistic illustration of the emergence mechanism of CI.
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来源期刊
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.10
自引率
5.70%
发文量
37
审稿时长
>12 weeks
期刊介绍: IJBIC discusses the new bio-inspired computation methodologies derived from the animal and plant world, such as new algorithms mimicking the wolf schooling, the plant survival process, etc. Topics covered include: -New bio-inspired methodologies coming from creatures living in nature artificial society- physical/chemical phenomena- New bio-inspired methodology analysis tools, e.g. rough sets, stochastic processes- Brain-inspired methods: models and algorithms- Bio-inspired computation with big data: algorithms and structures- Applications associated with bio-inspired methodologies, e.g. bioinformatics.
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