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Collective Behavior Coordination with Predictive Mechanisms

delete2008-01-01
delete78
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OA
AI
H
Hai‐Tao Zhang *
M
Michael Chen
G
Guy‐Bart Stan
周
周涛 (Tao Zhou)
J
J.M. Maciejowski
DOI:10.1109/MCAS.2008.928446delete
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Abstract

Abstract

En 中文
In natural flocks/swarms, it is very appealing that low-level individual intelligence and communication can yield advanced coordinated collective behaviors such as congregation, synchronization and migration. In the past few years, the discovery of collective flocking behaviors has stimulated much interest in the study of the underlying organizing principles of abundant natural groups, which has led to dramatic advances in this emerging and active research field. Inspired by previous investigations on the predictive intelligence of animals, insects and microorganisms, we seek in this article to understand the role of predictive mechanisms in the forming and evolving of flocks/swarms by using both numerical simulations and mathematical analyses. This article reviews some basic concepts, important progress, and significant results in the current studies of collective predictive mechanisms, with emphasis on their virtues concerning consensus improvement and communication cost reduction. Due to these advantages, such predictive mechanisms have great potential to find their way into industrial applications.
Keywords:
GROUP DECISION-MAKING
SMALL-WORLD NETWORKS
STABILITY ANALYSIS
SENSOR NETWORKS
ANIMAL GROUPS
SWARMS
AGENTS
BEES
CONSENSUS
DYNAMICS
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Circuits and Systems Magazine cover
IEEE Circuits and Systems Magazine
IF:
3.5
Papers:
530
Citations:
1.3K

Organization

U
University of Cambridge
Scholars:
7.7W
Papers: 7.1W
Citations: 13.7W
C
chinese academy of sciences
Scholars:
56.7W
Papers: 45.0W
Citations: 704
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