swarm intelligence systems
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SwarmSwarmIntelligenceIntelligence
SystemsSystems————————————————————————————
Christian JacobChristian [email protected]
Department of Computer ScienceUniversity of Calgary
CellularCellularAutomataAutomata
GlobalGlobal Effects from Local Effects from LocalRulesRules
Cellular AutomataCellular Automata
✦✦ The CA space is a lattice of cells with aThe CA space is a lattice of cells with aparticular geometry.particular geometry.
✦✦ Each cell contains a variable from aEach cell contains a variable from alimited 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.3
One-dimensional finite CA architectureOne-dimensional finite CA architecture
time
✦✦ K = 5 localK = 5 localconnectionsconnectionsper cellper cell
✦✦ SynchronousSynchronousupdate in discreteupdate in discretetime stepstime steps
A. Wuensche: The Ghost in the Machine, Artificial Life III, 1994. 4
Cellular Automata:Cellular Automata:Local Rules — Global EffectsLocal Rules — Global Effects
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--> 1D-CA Demos--> 1D-CA Demos
Time Evolution of the Time Evolution of the iithth Cell Cell
C f C C C C Cit
i Kt
it
it
it
i Kt( )
[ / ]( ) ( ) ( ) ( )
[ / ]( )( ,..., , , ,..., )+
− − + +=12 1 1 2
With periodic boundary conditions:
x C Cx N x< = +1: x N C C Nx x> = −:
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Value Range and Update RulesValue Range and Update Rules
✦✦ For For VV different states (= values) per cell different states (= values) per cellthere are there are VVKK permuations permuations of values in aof values in aneighbourhood neighbourhood of size K.of size K.
✦✦ The update function The update function ff can be can beimplemented as a lookup table with Vimplemented as a lookup table with VKK
entries, giving Ventries, giving VVVKK possible rules. possible rules.
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Example Update RuleExample Update Rule
✦✦ V = 2, K = 3V = 2, K = 3
✦✦ The rule table for rule The rule table for rule 3030::
111 110 101 100 011 010 001 000111 110 101 100 011 010 001 000 0 0 0 1 1 1 1 00 0 0 1 1 1 1 0
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See examples ...
2-D CA:2-D CA:Emergent Pattern FormationEmergent Pattern Formationin Excitable Mediain Excitable Media
Neuron excitationNeuron excitation
Neuron excitation (relaxed)Neuron excitation (relaxed)
HodgepodgeHodgepodge
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Cellular Automata
Random BooleanNetworks
Classifier Systems
SwarmSystems
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Hölldobler & Wilson, 1990
AntsAnts
Hölldobler & Wilson, 1990
Self-organizationTeam workCompetition...andHeavy Loads
Hölldobler & Wilson, 1990Teamwork Living Architecture
Hölldobler & Wilson, 1990
Living BridgeLiving Network
Hölldobler & Wilson, 1990Together we are strong ...
Ant ForagingAnt ForagingBehaviourBehaviour
Learning about EmergentLearning about EmergentSystemSystem Behaviours Behaviours
Experimental setupfor studying ant foreaging behaviour
Ant Ant ForeagingForeaging and Shortest Paths and Shortest Paths
Bonabeau et al., 1999
Shortest Path DiscoveryShortest Path Discovery
(a) Ants walking between nest(a) Ants walking between nestand food sitesand food sites
(b) An obstacle is placed in the(b) An obstacle is placed in themiddle.middle.
(c) Ants turn left or right, while(c) Ants turn left or right, whiledropingdroping pheromone ... pheromone ...
(d) … and finally the shortest(d) … and finally the shortestpath emerges.path emerges.
Adaptation to EnvironmentalAdaptation to EnvironmentalChangesChanges
(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
MassivelyMassively Parallel ParallelMicro WorldsMicro Worlds
StarLogoStarLogo
Mitchel ResnickMitchel Resnick (MIT, 1997) (MIT, 1997)
Agent-Based EvolutionAgent-Based Evolution
✦✦ Massive ParallelismMassive Parallelism
✦✦ Interacting AgentsInteracting Agents
✦✦ CooperationCooperation
✦✦ CompetitionCompetition
✦✦ Emergent SystemEmergent System Behaviour Behaviour
StarLogo Demo
Emergent SystemEmergent System Behaviour BehaviourSimulatedSimulated
Ant ForagingAnt Foraging
CollectiveCollectiveForagingForaging
EquidistantEquidistantFood SitesFood Sites
Randomly DistributedRandomly DistributedFood SitesFood Sites
Emergent SystemEmergent System Behaviour BehaviourSimulatedSimulated
Ant ForagingAnt Foraging
to look-for-foodif 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-foodif (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-nestif 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-nestif carrying-food? and ask patch-here [nest?] [set-carrying-food? False right 180 forward 1]end
DemoDemo
FollowingFollowing
BehaviourBehaviour
InteractionsInteractionsamongamong
Social InsectsSocial Insects
Interactions among Social InsectsInteractions among Social Insects
✦✦ Direct InteractionsDirect Interactions–– Food or liquid exchangeFood or liquid exchange
–– Visual or tactile, or Visual or tactile, or scentuousscentuous contactcontact
✦✦ Indirect InteractionsIndirect Interactions: : StigmergyStigmergy–– PheromonesPheromones
–– IndividualIndividual behaviour behaviour modifies themodifies theenvironment (e.g., by putting up environment (e.g., by putting up signs =signs =stigmastigma),), which in turn modifies the which in turn modifies thebehaviour behaviour of other individuals.of other individuals.
DemoDemo
ShepherdsShepherds
andand
SheepSheep
DemoDemo
StigmergyStigmergyinin
ActionAction
Bonabeau et al., 1999
ComplexComplex Systems Systems
EmergentEmergentBehaviours andBehaviours and Patterns Patterns
fromfromLocal InteractionsLocal Interactions
Stevens et al., 1988
Nuridsany & Pérennou, 1996
Ernst, 1998
Nuridsany & Pérennou, 1996
What to Learn from AntWhat to Learn from AntColonies as Complex SystemsColonies as Complex Systems
✦✦ Fairly simple units generateFairly simple units generatecomplicated global complicated global behaviourbehaviour..
✦✦ “If we knew how an ant colony works,“If we knew how an ant colony works,we might understand more about howwe might understand more about howall such systems work, from brains toall such systems work, from brains toecosystems.”ecosystems.”(Gordon, 1999)(Gordon, 1999)
Emergence in Complex SystemsEmergence in Complex Systems
✦✦ How do How do neuronsneurons respond to each other respond to each otherin a way that produces thoughts?in a way that produces thoughts?
✦✦ How do How do cellscells respond to each other in a respond to each other in away that produces the distinct tissues ofway that produces the distinct tissues ofa growing embryo?a growing embryo?
✦✦ How do How do speciesspecies interact to produce interact to producepredictable predictable changeschanges, over time, in, over time, inecological communities?ecological communities?
✦✦ ......
Swarm SystemsSwarm SystemsProvidingProviding NewNew Insights ... Insights ...
ReferencesReferences
✦ Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). SwarmIntelligence: From Natural to Artificial Systems. New York, OxfordUniversity Press.
✦ Ernst, A. M., ed. (1998). Digest: Kooperation und Konkurrenz,Heidelberg, Spektrum Akademischer Verlag.
✦ Gordon, D. (1999). Ants at Work. New York, The Free Press.✦ Hölldobler, B., and Wilson, E. O. (1990). The Ants. Cambridge,
MA, Harvard University Press.
✦ Nuridsany, C., and Pérennou, M. (1996). Microcosmos: TheInvisible World of Insects. New York, Stewart, Tabori & Chang.
✦ Resnik, M. (1997). Turtles, Termites, and Traffic Jams.Cambridge, MA, MIT Press.
✦ Stevens, C. F., et al. (1988). Gehirn und Nervensystem.Heidelberg, Spektrum Akademischer Verlag.