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[screen shot]

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If clicking does not initiate a download, try right clicking or control clicking and choosing "Save" or "Download".(The run link is disabled for this model because it was made in a version prior to NetLogo 6.0, which NetLogo Web requires.)

## WHAT IS IT?

This is a model to show how soft control work for the flocking model. Based on a Vicsek's model, a special agent--shill is added into the group to intervene the group behavior. You can control the shill by the mouse, so you can get the feeling of how to guide a group.

## HOW IT WORKS

The agent follow one rule: "alignment".

"Alignment" means that an agent tends to turn so that it is moving in the same direction that nearby birds are moving.

This rule only affect the agent's heading. Each agent always moves forward at the same constant speed.

## HOW TO USE IT

First, determine the number of agents you want in the simulation and set the POPULATION slider to that value. Press SETUP to create the agents, and press GO to have them start flying around.

The default settings for the sliders will produce reasonably good flocking behavior. However, you can play with them to get variations:

VISION is the distance that each bird can see 360 degrees around it.

NOISE is the noise range [-NOISE/2, NOISE/2], a random value from the range will be added to the angle for heading update each time step.

showLink will show the realtime neighborhood relationship amoung the agents.

Now, if you want to intervene the group, press 'add mouse-control-shill'. Then a shill is added and you can use the mouse to control its heading and the speed (you move the mouse faster will increase the speed of the shll).

You can also add some fixed heading shills by press the 'add'. Number of fixed heading shills are set by the slider of 'fixedHeadingShill'. Their headings are set by the slider of 'angle'.

## Idea of Soft-Control
The Program is to show the softcontrol idea we proposed in 2005. Related publications are

*Jing Han, Lin Wang. Nondestructive Intervention to Multi-agent Systems Through an Intelligent Agent. PLoS ONE 8(5): e61542. doi:10.1371/ journal.pone.0061542. May, 2013

Soft Control on Collective Behavior of a Group of Autonomous Agents by a Shill Agent.*Jing HAN, Ming LI, Lei GUO. Journal of Systems Science and Complexity, 19(1), pp54-62, 2006

Soft Control on Collective Behavior of a Group of Autonomous Agents by a Shill Agent.*Jing HAN, Ming LI, Lei GUO. Journal of Systems Science and Complexity, 19(1), pp54-62, 2006

Guiding a Group of Locally Interacting Autonomous Mobile Agents. *Han Jing, Guo Lei, Li Ming. Proceedings of the 24th Chinese Control Conference. South China University of Technology Press, Guangzhou, 184-187, July, 2005.

## HOW TO CITE

If you mention this model in a publication, we ask that you include these citations for the model itself and for the NetLogo software:

*Jing Han, Lin Wang. Nondestructive Intervention to Multi-agent Systems Through an Intelligent Agent. PLoS ONE 8(5): e61542. doi:10.1371/ journal.pone.0061542. May, 2013

This program is written by Han Jing, Academy of Mathematics and Systems Science, Chinese Academy Science, China. http://complex.amss.ac.cn/hanjing/. Email: hanjing@amss.ac.cn.

## RELATED MODELS

* Flocking

The code of this model is built based on the flocking model in the netlogo library.

Wilensky, U. (1998). NetLogo Flocking model. http://ccl.northwestern.edu/netlogo/models/Flocking. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Part of the code is modified and the softcontrol is added to the flocking model.

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