MULTI-CLUSTER FLOCKING BEHAVIOR FOR A CLASS OF CUCKER-SMALE MODEL WITH A PERTURBATION

AbstractThe goal of this paper is to study organized flocking behavior and systemic risk in heterogeneous mean-field interacting diffusions. We illustrate in a number of case studies the effect of heterogeneity in the behavior of systemic risk in the system, i.e., the risk that several agents default simultaneously as a result of interconnections. We also investigate the effect of heterogeneity on the “flocking behavior” of different agents, i.e., when agents with different dynamics end up following very similar paths and follow closely the mean behavior of the system. Using Laplace asymptotics, we derive an asymptotic formula for the tail of the loss distribution as the number of agents grows to infinity. This characterizes the tail of the loss distribution and the effect of the heterogeneity of the network on the tail loss probability.

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Flocking Behavior of Shiny Cowbirds (Molothrus bonariensis) At Feeding Areas During the Daily Cycle

The Wilson Journal of Ornithology ◽

10.1676/1559-4491-128.2.441 ◽

2016 ◽

Vol 128 (2) ◽

pp. 441-445 ◽

Cited By ~ 2

Author(s):

Gustavo H. Kattan ◽

Anamaría Posada ◽

Diego Fernando Arenas ◽

José Luis Moreno ◽

Ángela Barrera

Keyword(s):

Daily Cycle ◽

Molothrus Bonariensis ◽

Flocking Behavior

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FLOCKING STATES OF SELF-PROPELLING PARTICLES IN FREE SPACE

International Journal of Modern Physics B ◽

10.1142/s0217979207045013 ◽

2007 ◽

Vol 21 (23n24) ◽

pp. 3954-3959 ◽

Cited By ~ 2

Author(s):

KWAN-TAI LEUNG ◽

HSUAN-YI CHEN

Keyword(s):

Continuum Model ◽

Stationary Phases ◽

Formation Flight ◽

The Self ◽

Particle Interactions ◽

Simple Rules ◽

Range Alignment ◽

Steady State Solutions ◽

Present Particle ◽

Flocking Behavior

We present particle-based simulations for the flocking behavior of self-propelling particles. Built upon previous models, our models include realistic but simple rules for the self-propelling, drag, and inter-particle interactions. Depending on both the strength and range of the interactions, a host of stationary phases appear, including independent wandering, formation flight, swarm, and rotating vortex. Of particular interest, we determine that the rotating flock can only arise in the absence of long-range alignment. We also construct a phenomenological continuum model and obtain steady-state solutions for the rotating state.

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Mesotocin and Nonapeptide Receptors Promote Estrildid Flocking Behavior

Science ◽

10.1126/science.1174929 ◽

2009 ◽

Vol 325 (5942) ◽

pp. 862-866 ◽

Cited By ~ 142

Author(s):

J. L. Goodson ◽

S. E. Schrock ◽

J. D. Klatt ◽

D. Kabelik ◽

M. A. Kingsbury

Keyword(s):

Flocking Behavior

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Dynamic Pathfinding for Non-Player Character Follower on Game

Jurnal Teknik Informatika C.I.T Medicom ◽

10.35335/cit.vol13.2021.68.pp51-58 ◽

2021 ◽

Vol 13 (2) ◽

pp. 55-63

Author(s):

Paulus Harsadi ◽

Siti Asmiatun ◽

Astrid Novita Putri

Keyword(s):

Video Game ◽

Potential Field ◽

Computation Time ◽

Attractive Potential ◽

Artificial Potential Field ◽

Field Function ◽

Particle Chain ◽

Comparison Algorithms ◽

Test Result ◽

Flocking Behavior

Artificial Intellegences in video game are important things that can challenge game player. One of them is creating character or NPC Follower (Non-player character Follower) inside the video game, such as real human/animal attitude. Artificial Intelligences have some techniques in which pathfinding is one of Artificial Intellegence techniques that is more popular in research than other techniques. The ability to do dynamic pathfinding is Dynamic Particle Chain (DPC) algorithm. This algorithm has the ability of flocking behavior called boid to explore the environment. But, the algoritm method moves from one boid’s point to another according to the nearest radius, then it will be able to increase computation time or needed time toward the target. To finish higher computation problem in dynamic pathfinding, the researcher suggests an algorithm that is able to handle dynamic pathfinding process through attractive potential field function of Artificial Potential Field to start pathfinding toward the target and flocking behavior technique to avoid the obstacle. Based on the test result by simulation of moving environment and complex, the computation time of algorithm is faster than comparison algorithms, DPC and Astar. It concludes that the suggested method can be used to decrease computation level in dynamic pathfinding.

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Flocking behavior of the Cucker-Smale model under rooted leadership in a large coupling limit

Quarterly of Applied Mathematics ◽

10.1090/s0033-569x-2014-01350-5 ◽

2014 ◽

Vol 72 (4) ◽

pp. 689-701 ◽

Cited By ~ 4

Author(s):

Seung-Yeal Ha ◽

Zhuchun Li ◽

Marshall Slemrod ◽

Xiaoping Xue

Keyword(s):

Large Coupling ◽

Flocking Behavior

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Illustration of Centralized Command and Control for Flocking Behavior

International Journal of Handheld Computing Research ◽

10.4018/ijhcr.2014040101 ◽

2014 ◽

Vol 5 (2) ◽

pp. 1-22

Author(s):

Sami Oweis ◽

Subramaniam Ganesan ◽

Ka C Cheok

Keyword(s):

Obstacle Avoidance ◽

Command And Control ◽

Flow Chart ◽

Controlled Systems ◽

Swarm Robots ◽

Group A ◽

Swarming Behavior ◽

And Control ◽

Flocking Behavior ◽

Ground Systems

Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems - ‘'Boids''- such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a building to reach a target. The flocking behavior is implemented on a server-based control, which processes each of the boids' properties e.g. position, speed & target. Subsequently, the server will assign the appropriate move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to Reynolds three original rules for flocking, two other rules- targeting obstacle avoidance - are presented-. Our result shows that the obstacles' avoiding rule was utilized to ensure that the flock didn't collide with obstacles in each of the boids' paths.

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