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Difference between revisions of "Placement Into a Flock"

(Flock Behavior with No Influencing Agents)
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==Flock Behavior with No Influencing Agents==
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==Drop Case==
  
The following video shows the behavior of the flock when not being influenced by any influencing agents.  In this case, each agent orients itself towards the average heading of its neighbors.
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In the Drop case, we are able to initially drop each influencing agent into the flock at whatever location we desire.
  
This video depicts 4 separate trials, where each trial is concluded when the flock has converged to travelling at a particular heading.  
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In our AAMAS paper, we consider four different methods in which to determine these initial positions at which to drop the influencing agents.  Below we present videos of each of these methods.  We show videos in which the number of agents (''n'') is 10, since this is one value of ''n'' that was used to obtain the data presented in Figure 5 in our paper.
  
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As we present videos below, note that we use the same notation as in the paper.  Specifically, the number of agents is represented by ''n'', the number of influencing agents is represented by ''k'', and the number of flocking agents is represented by m, such that ''k''+''m''=''n''.  For the videos shown below, we always use a random seed of 1 for calculating the initial flocking agent positions.
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On the bottom right of the videos, you will notice that some numbers are printed out part way though each trial.  These numbers are (from left to right): the number of time steps for the flock to converge, a number irrelevant to these trials (always 0.0), the number of flocking agents 'lost', the x-axis average distance from each not 'lost' flocking agent to the center of the flocking agents that are not 'lost', and the y-axis average distance from each not 'lost' flocking agent to the center of the flocking agents that are not 'lost'.
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'''''n''=10, k=1, ''m''=9 -> 10% of flock is composed of influencing agents'''
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''Random Placement''
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''Grid Placement''
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''Border Approach''
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''Graph Approach''
 
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Revision as of 12:57, 16 February 2015

This page complements our AAMAS 2015 paper.

Flock Behavior with No Influencing Agents

The following video shows the behavior of the flock when not being influenced by any influencing agents. In this case, each agent orients itself towards the average heading of its neighbors.

This video depicts 4 separate trials, where each trial is concluded when the flock has converged to travelling at a particular heading.

Drop Case

In the Drop case, we are able to initially drop each influencing agent into the flock at whatever location we desire.

In our AAMAS paper, we consider four different methods in which to determine these initial positions at which to drop the influencing agents. Below we present videos of each of these methods. We show videos in which the number of agents (n) is 10, since this is one value of n that was used to obtain the data presented in Figure 5 in our paper.

As we present videos below, note that we use the same notation as in the paper. Specifically, the number of agents is represented by n, the number of influencing agents is represented by k, and the number of flocking agents is represented by m, such that k+m=n. For the videos shown below, we always use a random seed of 1 for calculating the initial flocking agent positions.

On the bottom right of the videos, you will notice that some numbers are printed out part way though each trial. These numbers are (from left to right): the number of time steps for the flock to converge, a number irrelevant to these trials (always 0.0), the number of flocking agents 'lost', the x-axis average distance from each not 'lost' flocking agent to the center of the flocking agents that are not 'lost', and the y-axis average distance from each not 'lost' flocking agent to the center of the flocking agents that are not 'lost'.

n=10, k=1, m=9 -> 10% of flock is composed of influencing agents Random Placement

Grid Placement

Border Approach

Graph Approach