Faculty Opinions recommendation of Mesotocin and nonapeptide receptors promote estrildid flocking behavior.

2009 ◽  
Author(s):  
Randy Nelson ◽  
Zachary Weil
Keyword(s):  
Flocking Behavior

scholarly journals On the effect of heterogeneity on flocking behavior and systemic risk

2017 ◽  
Vol 34 (3-4) ◽  
Author(s):  
Fei Fang ◽  
Yiwei Sun ◽  
Konstantinos Spiliopoulos
Keyword(s):  
Asymptotic Formula ◽  
Case Studies ◽  
Systemic Risk ◽  
Mean Field ◽  
Loss Probability ◽  
Loss Distribution ◽  
Interacting Diffusions ◽  
The Mean ◽  
Laplace Asymptotics ◽  
Flocking Behavior

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.

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

2016 ◽  
Vol 128 (2) ◽  
pp. 441-445 ◽  
Author(s):  
Gustavo H. Kattan ◽  
Anamaría Posada ◽  
Diego Fernando Arenas ◽  
José Luis Moreno ◽  
Ángela Barrera
Keyword(s):  
Daily Cycle ◽  
Molothrus Bonariensis ◽  
Flocking Behavior

FLOCKING STATES OF SELF-PROPELLING PARTICLES IN FREE SPACE

2007 ◽  
Vol 21 (23n24) ◽  
pp. 3954-3959 ◽  
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.

scholarly journals Mesotocin and Nonapeptide Receptors Promote Estrildid Flocking Behavior

Science ◽  
2009 ◽  
Vol 325 (5942) ◽  
pp. 862-866 ◽  
Author(s):  
J. L. Goodson ◽  
S. E. Schrock ◽  
J. D. Klatt ◽  
D. Kabelik ◽  
M. A. Kingsbury
Keyword(s):  
Flocking Behavior

scholarly journals Dynamic Pathfinding for Non-Player Character Follower on Game

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.

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

2014 ◽  
Vol 72 (4) ◽  
pp. 689-701 ◽  
Author(s):  
Seung-Yeal Ha ◽  
Zhuchun Li ◽  
Marshall Slemrod ◽  
Xiaoping Xue
Keyword(s):  
Large Coupling ◽  
Flocking Behavior

Illustration of Centralized Command and Control for Flocking Behavior

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.

Energy-Balanced Leader-Switching Policy for Formation Rotation Control of Multi-Agent Systems Inspired by Bird Flocks

2019 ◽  
Author(s):  
Corey Dotson ◽  
Geronimo Macias ◽  
Kooktae Lee
Keyword(s):  
Formation Control ◽  
Consensus Algorithm ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Switching Algorithm ◽  
Multi Agent ◽  
Migrating Birds ◽  
Switching Method ◽  
Rotation Strategy ◽  
Flocking Behavior

Abstract This paper addresses an energy-balanced leader-switching policy for formation rotation control of multi-agent systems inspired by bird flocks. Birds that flock in V-formation with a leader rotation strategy are able to travel longer distances due to reduced drag and therefore less energy expenditure. This flocking behavior with a leader rotation will result in more conservation of overall energy and will be particularly beneficial to migrating birds that should fly long distances without landing. In this paper, we propose an energy-balanced leader-switching policy inspired by this bird flocking behavior in order to increase the flight range for multi-agent systems. The formation control of multi-agent systems is achieved by the consensus algorithm, which is fully decentralized through the use of information exchanges between agents. The proposed leader-switching method is not necessarily incorporated with the consensus dynamics and thus, the leader-switching algorithm can be decoupled from formation control dynamics. Therefore, the proposed method can simplify the leader-switching algorithm, making it easy to implement. Moreover, we propose the analytic flight distance based on the energy consumption model for each agent. To test the validity of the developed method, several simulation results are presented.

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