Swarm Swarm Intelligence Intelligence Systems Systems - - PowerPoint PPT Presentation

Swarm Swarm Intelligence Intelligence Systems Systems

—————————————— —————————————— Christian Jacob Christian Jacob

jacob@cpsc.ucalgary.ca

Department of Computer Science University of Calgary

Cellular Cellular Automata Automata

Global Global Effects from Local Effects from Local Rules Rules

Cellular Automata Cellular Automata

✦ ✦ The CA space is a lattice of cells with a

The CA space is a lattice of cells with a particular geometry. particular geometry.

✦ ✦ Each cell contains a variable from a

Each cell contains a variable from a limited range (e.g., 0 and 1). limited range (e.g., 0 and 1).

✦ ✦ All cells update synchronously.

All cells update synchronously.

✦ ✦ All cells use the same updating rule,

All cells use the same updating rule, depending only on local relations. depending only on local relations.

✦ ✦ Time advances in discrete steps.

Time advances in discrete steps.

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One-dimensional finite CA architecture One-dimensional finite CA architecture

time ✦ ✦ K = 5 local

K = 5 local connections connections per cell per cell

✦ ✦ Synchronous

Synchronous update in discrete update in discrete time steps time steps

• A. Wuensche: The Ghost in the Machine, Artificial Life III, 1994.

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Cellular Automata: Cellular Automata: Local Rules — Global Effects Local Rules — Global Effects

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2-D CA: 2-D CA: Emergent Pattern Formation Emergent Pattern Formation in Excitable Media in Excitable Media

Neuron excitation Neuron excitation Neuron excitation (relaxed) Neuron excitation (relaxed) Hodgepodge Hodgepodge

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Cellular Automata Random Boolean Networks Classifier Systems Swarm Systems

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Hölldobler & Wilson, 1990

Ants Ants

Hölldobler & Wilson, 1990

Self-organization Team work Competition ... and Heavy Loads

Ant Foraging Ant Foraging Behaviour Behaviour

Learning about Emergent Learning about Emergent System System Behaviours Behaviours

Experimental setup for studying ant foreaging behaviour

Ant Ant Foreaging Foreaging and Shortest Paths and Shortest Paths

Bonabeau et al., 1999

Shortest Path Discovery Shortest Path Discovery

(a) Ants walking between nest (a) Ants walking between nest and food sites and food sites (b) An obstacle is placed in the (b) An obstacle is placed in the middle. middle. (c) Ants turn left or right, while (c) Ants turn left or right, while droping droping pheromone ... pheromone ... (d) … and finally the shortest (d) … and finally the shortest path emerges. path emerges.

Adaptation to Environmental Adaptation to Environmental Changes Changes

(a) The newly found shortest path (a) The newly found shortest path (b) Moving the obstacle (b) Moving the obstacle (c) Discovery of new shortest path (c) Discovery of new shortest path

Massively Massively Parallel Parallel Micro Worlds Micro Worlds StarLogo StarLogo

Mitchel Resnick Mitchel Resnick (MIT, 1997) (MIT, 1997)

Agent-Based Evolution Agent-Based Evolution

✦ ✦ Massive Parallelism

Massive Parallelism

✦ ✦ Interacting Agents

Interacting Agents

✦ ✦ Cooperation

Cooperation

✦ ✦ Competition

Competition

✦ ✦ Emergent System

Emergent System Behaviour Behaviour

Emergent System Emergent System Behaviour Behaviour

Simulated Simulated Ant Foraging Ant Foraging

Collective Collective Foraging Foraging Equidistant Equidistant Food Sites Food Sites Randomly Distributed Randomly Distributed Food Sites Food Sites

Emergent System Emergent System Behaviour Behaviour

Simulated Simulated Ant Foraging Ant Foraging

to look-for-food if not carrying-food? [ifelse (ask patch-here [pheromone]) < 0.2 [right random 40 left random 40] [set-heading uphill pheromone] forward 1] end to find-food if (not carrying-food?) and ask patch-here [food > 0] [set-carrying-food? True ask patch-here [set-food food - 1] set-drop-size 35 right 180 forward 1] end to return-to-nest if carrying-food? [ask patch-here [add-pheromone-drop] set-drop-size drop-size - 0.6 set-heading uphill nest-scent forward 1] end to find-nest if carrying-food? and ask patch-here [nest?] [set-carrying-food? False right 180 forward 1] end

Demo Demo

Following Following Behaviour Behaviour

Interactions Interactions among among Social Insects Social Insects

Interactions among Social Insects Interactions among Social Insects

✦ ✦ Direct Interactions

Direct Interactions

– – Food or liquid exchange Food or liquid exchange – – Visual or tactile, or Visual or tactile, or scentuous scentuous contact contact – – Pheromones Pheromones

✦ ✦ Indirect Interactions:

Indirect Interactions: Stigmergy Stigmergy

– – Individual Individual behaviour behaviour modifies the modifies the environment (e.g., by putting up environment (e.g., by putting up signs = signs = stigma stigma), ), which in turn modifies the which in turn modifies the behaviour behaviour of other individuals.

• f other individuals.

Demo Demo

Stigmergy Stigmergy in in Action Action

Bonabeau et al., 1999

What to Learn from Ant What to Learn from Ant Colonies as Complex Systems Colonies as Complex Systems

✦ ✦ Fairly simple units generate

Fairly simple units generate complicated global complicated global behaviour behaviour. .

✦ ✦ “If we knew how an ant colony works,

“If we knew how an ant colony works, we might understand more about how we might understand more about how all such systems work, from brains to all such systems work, from brains to ecosystems.” ecosystems.” (Gordon, 1999) (Gordon, 1999)

Emergence in Complex Systems Emergence in Complex Systems

✦ ✦ How do

How do neurons neurons respond to each other respond to each other in a way that produces thoughts? in a way that produces thoughts?

✦ ✦ How do

How do cells cells respond to each other in a respond to each other in a way that produces the distinct tissues of way that produces the distinct tissues of a growing embryo? a growing embryo?

✦ ✦ How do

How do species species interact to produce interact to produce predictable predictable changes changes, over time, in , over time, in ecological communities? ecological communities?

✦ ✦ ...

...

Swarm Systems Swarm Systems Providing Providing New New Insights ... Insights ...

References References

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Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York, Oxford University Press.

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Ernst, A. M., ed. (1998). Digest: Kooperation und Konkurrenz, Heidelberg, Spektrum Akademischer Verlag.

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Gordon, D. (1999). Ants at Work. New York, The Free Press.

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Hölldobler, B., and Wilson, E. O. (1990). The Ants. Cambridge, MA, Harvard University Press.

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Nuridsany, C., and Pérennou, M. (1996). Microcosmos: The Invisible World of Insects. New York, Stewart, Tabori & Chang.

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Resnik, M. (1997). Turtles, Termites, and Traffic Jams. Cambridge, MA, MIT Press.

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Stevens, C. F., et al. (1988). Gehirn und Nervensystem. Heidelberg, Spektrum Akademischer Verlag.