1. Co-Fields: Towards a Unifying Approach to the Engineering of Swarm Intelligent Systems
Marco Mamei
Franco Zambonelli
Letizia Leonardi
ESAW '02
Co-Fields & Swarms

2. Motivations
Swarm intelligence can provide useful sources of inspiration to designing multi agent applications.
However it is very difficult to move from just a collection of examples to a general engineering methodology.
Unifying abstractions for for large classes of swarm intelligent systems is a prerequisite for such a general methodology.
ESAW '02
Co-Fields & Swarms

3. Co-Fields Model
Co-Fields is a model for motion coordination in a multi agent system.
In the Co-Fields model, agents live in an environment which is described by fields (distributed data structures) that can be spread either by the agents themselves or by the environment.
ESAW '02
Co-Fields & Swarms

4. Co-Fields Model
Agents combine the fields they sense to obtain a task dependent field (referred as the coordination field) and then move by following the gradient of such combined field.
ESAW '02
Co-Fields & Swarms

5. Co-Fields as a Unifying Abstarction
A Co-Fields based system is a simple dynamical system. Agents are simply seen as balls rolling upon a surface whose shape is described by the coordination field. Complex movements are achieved not because of the agent will, but because dynamic re-shaping of this surface.
The claim of this talk is that Co-Fields can provide a unifying abstraction to describe swarm intelligence exemples.
ESAW '02
Co-Fields & Swarms

6. Swarm Intelligent Strategies
Wolves Surrounding a Prey
AI in video games, Robot coordination
Birds Flocking
Air Traffic control
Ant Foraging
Routing in telecommunication networks
Ant Division of Labor
Multitasking
ESAW '02
Co-Fields & Swarms

7. Wolves Surrounding a Prey Natural Explanation
Wolves simply hunt for a moose trying to maintain a suitable distance from other wolves.
Simulations have shown that following this simple strategy, wolves are able to surround the prey.
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Co-Fields & Swarms

8. Wolves Surrounding a Prey Co-Fields Explanation
The moose and the wolves, propagates these kinds of fields:
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Co-Fields & Swarms

9. Wolves Surrounding a Prey Co-Fields Explanation
Then they compute the following coordination fields and follows the gradient downhill
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Co-Fields & Swarms

10. Testing Co-Fields Algorithms
Differential Equations
Simulations (test the problem in constrained environments)
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Co-Fields & Swarms

11. Wolves Surrounding a Prey Solving the Differential Equations
Numerically solving the differential equation:
Wolves repeal each other: surrounding
Wolves do not repeal each other: NO surrounding
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Co-Fields & Swarms

12. Birds Flocking Natural Explanation
The coordinated behavior of flocks can be explained by assuming that each bird tries to maintain a specified distance (the one that offer best flight conditions) from the nearest birds.
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Co-Fields & Swarms

13. Birds Flocking Co-Fields Explanation
Each bird in the flock propagates the following field (repeal at short distances, attracts on long distances):
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14. Birds Flocking Co-Fields Explanation
Then they compute the following coordination field and follows the gradient downhill
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Co-Fields & Swarms

15. Flocking Solving the Differential Equations
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16. Flocking MAS Simulation. . . . . . . . .
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Co-Fields & Swarms

17. Flocking MAS Simulation. . . . . . . . .
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Co-Fields & Swarms

18. Ants Foraging Natural Explanation
Ants lay down pheromone trails to guide other ants towards food or back to the anthill.
Ants wander randomly but are attracted by pheromones.
Food pheromone is laid down when returning form a food source
Nest pheromone is laid down when leaving the anthill
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Co-Fields & Swarms

19. Ants Foraging Co-Fields Explanation
The environment spread and maintain two initially flat fields: Food and Nest fields
The environment reacts to ants’ movement by wrinckling the fields’ surface.
Ants’ movements are affected by the wrinckles
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Co-Fields & Swarms

20. Ants Foraging Co-Fields Explanation
Analytical description of a wrinkle:
K(0) is dynamically set so as to be lower that all the neighboring wrinkles, to create steepness
K(t) goes to 0 as t increases to accustom for “evaporation”
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Co-Fields & Swarms

21. Ant Division of Labor Natural Explanation
Each individual ant has a response-treshold for every task.
It engages in task performance when the level of the task associated stimuli exceeds the treshold.
It drops a task when the task associated stimuli falls under another treshold.
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Co-Fields & Swarms

22. Ant Labor Division Co-Fields Explanation
We can imagine that each ant is embedded in an abstract task-space.
Movement in this space are not actual movement, but rather change on duties.
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Co-Fields & Swarms

23. Ant Labor Division Co-Fields Explanation
The environment generates fields encoding the stimuli encouraging ants in performing a task.
Ants move in this space by following the task fields downhill.
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Co-Fields & Swarms

24. Conclusions
We have presented a unifying abstraction to deal with swarm intelligent system; resembling: visual, smell, pheromones, air turbolence, task-stimuli.
It is a prerequisite for a general engineering methodology.
ESAW '02
Co-Fields & Swarms

25. Future Works Theoretical Investigations
Dynamical Systems Analysis
Relationship with System Theory
Other examples and better formalization of the current ones
Towards true Engineering Principles…
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Co-Fields & Swarms

26. Further Info
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Co-Fields & Swarms