Emergent Behavior in Biological Swarms Stephen Motter.

Emergent Behavior in Biological Swarms

Stephen Motter

The Papers

• The Self-Organizing Exploratory Pattern of the Argentine Ant

• Study of Group Food Retrieval by Ants as a Model for Multi-Robot Collective Transport

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Paper 1

• Authors:– J. L. Deneubourg– S. Aron– S. Goss– J. M. Pasteels

• Appeared in the Journal of Insect Behavior, 1990

The Self-Organizing Exploratory Pattern of the Argentine Ant

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Problem

• How do ants explore?– Rather, how is a single ant’s exploration

affected by the previous ants? Is it a function? Can we model it?

• Homogenous/heterogeneous agents?

Paper 1 CritiqueResultsExperime

ntApproachInsights

Problem Statement

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InsightsProblem

• Ants explore with no fixed destination.

• They do this at night (so no visual cues).

• The Argentine ant lays her pheromone continuously (not just on return).


ntApproach

Insights

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ApproachProblem

1) Observe the exploratory pattern.2) Reduce to a binary choice (diamond

bridge).3) Generate a model from observed data.– Does a Monte-Carlo model fit?– General choice function:


ntInsights

Approach

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Experiment

Problem

• This experiment has two parts.

• Empty arena (no food or debris).

• Automatically photographed every 60 seconds.

• Sand periodically replaced.

Paper 1 CritiqueResultsApproachInsights

Experiment (Open Arena)

Note: This is an artist’s rendition of the experiment, as no image of the arena was provided by the authors. 6

Experiment

Problem

• The second part is more controlled.• Ants crossing bridge counted every

3-minutes.• Ants prevented from doubling back.


Experiment (Diamond Bridge)

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ResultsProblem

• Ants explore close to the nest first.

• The front advances, but leaves a trail.

• Number of explorers grows logistically.

• Picking out returning explorers halts exploration development.

• Ants will not ‘re-explore’ a well-explored area.

Paper 1 CritiqueExperime

ntApproachInsights

Results (Open Arena)

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ResultsProblemPaper 1 CritiqueExperime

ntApproachInsights

Results (Open Arena)

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ResultsProblem

• Both branches chosen equally at first.

• Positive feedback rapidly makes one path preferable.

• Ants act reactively (as a function of # ant passages).


ntApproachInsights

Results (Diamond Bridge)

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ntApproachInsights

Results (Diamond Bridge)

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(Note: The axes on these graphs are not the same)

CritiqueProblem

• The model fits, but a lot of simplifications are required.

• Pheromone quantity estimated by number of ants passing (ignores evaporation, assumes each ant lays equal amount of pheromone).

• The ‘separated ants’ appear more dispersed in experiments than model.

Paper 1 ResultsExperime

ntApproachInsights

Critique

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Paper 2Study of Group Food Retrieval by Ants as a Model for Multi-Robot

Collective Transport• Authors:– S. Berman– Q. Lindsey– V. Kumar–M. S. Sakar– S. C. Pratt

• Appeared in the Proceedings of the IEEE, 2011

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Problem

• What is the role of each ant in collective transport? Rules that govern their actions?

• Can we apply this to robots who, like ants. have limited sensing, communication, and computation capabilities?


ntApproachInsights

Problem Statement

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InsightsProblem

• Ants grab stuff in groups (better than robots do).

• The ant approach is decentralized, scalable, a requires no a priori information.

• Therefore, ants are more flexible and more robust than centralized approach.

• Prey transport teams are superefficient.


ntApproach

Insights

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ApproachProblem

1) Observe ants in a controlled environment.

2) Develop a behavior model.3) Run a simulation to see if the model

matches.


ntInsights

Approach

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Experiment

Problem

• Fabricate fake food (out of springs and fig paste) and measure the forces and deformations as ants carry it back to the nest (about 1 meter).

• 27 Trials


Experiment

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ntApproachInsights

Results (Observation)

• Quasi-static motion• More ants is better

(faster)• Load speed saturation

with increased group size

ResultsProblem

• Hybrid system with probabilistic transitions between two task modes:– search for grasp point– transport

• Start from uniformly randomly distributed positions and orientations


ntApproachInsights

Results (Simulation)

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CritiqueProblem

• Friction is a major factor which throws the deformation measures off.

• They even observe “stick-slip” motion.

Paper 2 ResultsExperime

ntApproachInsights

Critique

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Closing Thoughts

• Both use ants as a model of homogenous agents and minimal communication.

• Both attempt to apply lessons from ants to distributed robotics.

• Both simulations use very simple models, while still being reasonably accurate.

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Questions?

• The Self-Organizing Exploratory Pattern of the Argentine Ant

• Study of Group Food Retrieval by Ants as a Model for Multi-Robot Collective Transport

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Emergent Behavior in Biological Swarms Stephen Motter.

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