collective intelligence - a brief...

Collective Intelligencea brief introduction

Gauthier Picard

MINES Saint-ÉtienneLaHC UMR CNRS 5516

[email protected]

mailto:[email protected]

Preliminary: install NetLogo

http://ccl.northwestern.edu/netlogo/


Today’s Menu

What’s a Collective Intelligence?

Some Example in the Nature

StigmergyAnt Colony OptimizationAnt Foraging in NetlogoSocial Spiders

Aggregation Behaviors (flocking)BOIDSFlocking Behavior in Netlogo

Gauthier Picard Collective Intelligence 3

Some Ideas in Bulk...

Collective, communauty

Agents, subparts

Convergence, common goal

Mulitple interactions, stigmergy

Local vs. global

Local information, bounded rationality

Simple rules

Shared environment

Complexity, emergent behaviors

. . .


Today’s Menu






Animal Collective IntelligenceAnts, Wasps


Animal Collective IntelligenceTermites, Humans


Animal Collective IntelligenceCrustaceans, Ants (again...)


Animal Collective IntelligenceFishes, Birds


Animal Collective IntelligenceMammals


Artificial Collective Intelligence

How to design artificial collectives?

How to make artificial intelligences cooperate?

How to design artificial agents able to work jointly?

⇒ Models, algorithms and engineering paradigms


Bio-inspired Algorithms

PrincipleTaking inspiration from collective behaviors observed in the Nature to design algorithms

Example (Some models)

Ant Colony Optimization [Dorigo et al., 1996]

Image Processing with Social Spiders [Bourjot et al., 2003]

Flocking and swarming behaviors [Reynolds, 1987]

Observe Nature Model behaviors Design algorithmApply to real

problem


Some Examples of Collective Behaviors


Some Examples of Collective Behaviors (cont.)


Today’s Menu






Stigmergy

« The work excites the worker » [Grassé, 1959]

→ Behaviourist explanation indirect stimulus-responses← Observation on termites building behaviour

ConsequencesI Direct interactions not necessary to coordinate the work of a groupI Indirect interactions are su�icientI Indirect communication indirect between agents by the environment

In social animals: termites, ants, bees, wasps, spiders, rats, etc.I Building behaviourI RecruitmentI Division of labourI Prey transportI etc.


Stigmergy Requirements

Stigmergy ElementsI Environment

I Central roleI Dynamics

I Individual interacting agentsI Capabilities to move, perceive and act in the environmentI Actions in the environment not for the others agents

Stigmergy DesignI Definition of the environment

I What is perceived by agentsI Which changes can be done by agentsI What is the duration of the information: evaporation

I Definition of the agentsI How do they moveI What they can do in the environmentI In which state must they be to act: probalistic values


Stigmergic MechanismsMulti-Agent Applications

Travelling salesman problem (TSP)[Dorigo et al., 1996]

Computer network management, Ants foraging[Foukia and Hassas, 2004]

Network routing, Ants foraging[Di Caro and Dorigo, 1998]

Supply Network Management[Reitbauer et al., 2004]

Coordination of unmanned vehicles[Parunak et al., 2002]

Manufacturing control, Ants foraging[Armetta et al., 2004; Brueckner, 2000]

Mobile Ad-hoc NETworks[Brueckner and Parunak, 2004]


Ant Algorithms[Dorigo et al., 1996]

Probabilistic technique (metaheuristic)I Solving combinatorial problemsI Finding good paths through graphs

Stigmergic mechanism: pheromone trailsI Deposited when food is foundI Attracts ants (probabilistically)↓ Evaporates when no more used (bad source)↑ Reinforced when frequently used (good source


Ant Colony Optimization (ACO)

Arc Selection

pki,j(t) =

τij(t)

αηβij∑l∈Jk

iτil(t)αη

βil

if j ∈ Jki

0 if j /∈ Jki

Pheromone Deposited

∆kij(t) =

{Q

Lk(t)if (i, j) ∈ T k(t)

0 if (i, j) /∈ T k(t)

Pheromone Update

τij(t+ 1) = (1− ρ)τij(t) +

m∑k=1

∆kij(t)

where:

Jki , possible moves from i

ηij , visibility (= 1/dij )

τij(t), amount of pheromone onarc i,j

α and β, parameters

T k(t), visited arcs at time t

Lk(t), length of T k(t)

Q, parameter

m, number of ants

ρ, parameter


Illustration with NetLogo




Sample Application: Collective Robotics


Social Spiders (Anelosimus Eximius)[Bourjot et al., 2003]

Spiders are attracted by silk and by their other congenersSeveral individual spiders can succeed each other to build a web


Social SpidersModeling Issue

EnvironmentI Square grid composed of stakes with di�erent

heightsI Initially without threadI Dynamical additions of spin threads

AgentsI Moving from one stake to anotherI Attraction by silk→ contextual choice

(probabilistic) of a given motion (function of thenumber of threads)

I Putting silk at the top of a stake


Social SpidersSystem Dynamic

Coordination by StigmergyI Implicitly modelled in the behaviorI Motion influenced by silkI More there is silk in a position, and greater is the chance to be chosenI No centralisation, no social reference

I Dynamic relevant to individual and social spiders


Social SpidersApplication to Image Segmentation

5.3. Raw results As the following pictures bring to light, our approach gives satisfying results when

parameters of the spider-model have been accurately and empirically tuned by trials and errors.

Although the grid is not well “detached” in the environment the algorithm provides good

results even if the region is not fully covered (figures 6 and 7), it must be noticed that Alain’s hair is also well extracted (figures 8 and 9). Figure 10 shows different regions our approach is able to extract from Alain’s image.


Today’s Menu






Aggregation Behaviors (flocking)

Flock of birds, school of fish, or swarm of insects

Realistic simulation of complex global behaviour with simple local behaviours

First simulated in Boids [Reynolds, 1987]

Flocking rules

Separation avoid crowding neighbours

Alignment steer towards average heading of neighbours

Cohesion steer towards average position of neighbours


Illustration avec NetLogo




That’s all folks!

Do not hesitate to contact me: [email protected]


mailto:[email protected]

References

Armetta, F., S. Hassas, S. Pimont, and E. Gonon (2004). “Managing Dynamic Flows in Production Chains ThroughSelf-Organization”. In: Engineering Self-Organising Systems: Methodologies and Applications. Vol. 3464. Lecture Notes inComputer Science (LNCS). Springer, pp. 240–255.

Bourjot, C., V. Chevrier, and V. Thomas (2003). “A New Swarm Mechanism based on Social Spiders Colonies : from WebWeaving to Region Detection”. In: Web Intelligence and Agent Systems: An International Journal (WIAS) 1.1, pp. 47–64.

Brueckner, S. (2000). “Return from the Ant: Synthetic Ecosystems for Manufacturing Control”. PhD thesis. Department ofComputer Science, Humboldt University Berlin.

Brueckner, S. and H. V. D. Parunak (2004). “Self-Organizing MANET Management”. In: Engineering Self-Organising Systems,Nature-Inspired Approaches to Software Engineering [revised and extended papers presented at the EngineeringSelf-Organising Applications Workshop, ESOA 2003, held at AAMAS 2003 in Melbourne, Australia, in July 2003 and selectedinvited papers from leading researchers in self-organisation]. Vol. 2977. Lecture Notes in Computer Science (LNCS). Springer,pp. 20–35.

Di Caro, G. and M. Dorigo (1998). “Ant Colonies for Adaptive Routing in Packet-Switched Communications Networks”. In:Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN V). Lecture Notes in ComputerScience (LNCS) 1498. London, UK: Springer-Verlag, pp. 673–682.

Dorigo, M., V. Maniezzo, and A. Colorni (1996). “"The Ant System: Optimization by a Colony of Cooperating Agents"”. In: IEEETransactions on Systems, Man, and Cybernetics Part B: Cybernetics 26.1, pp. 29–41.

Foukia, N. and S. Hassas (2004). “Managing Computer Networks Security through Self-Organization: A Complex SystemPerspective”. In: Engineering Self-Organising Systems, Nature-Inspired Approaches to Software Engineering. Vol. 2977. LectureNotes in Computer Science (LNCS). Springer, pp. 124–138.


References (cont.)Grassé, P. (1959). “La reconstruction du nid et les interactions inter-individuelles chez les bellicositermes natalenis etcubitermes sp. la théorie de la stigmergie: essai d’interprétation des termites constructeurs”. In: Insectes Sociaux 6, pp. 41–83.

Karuna, H., P. Valckenaers, B. Saint Germain, P. Verstraete, C. B. Zamfirescu, and H. Van Brussel (2004). “EmergentForecasting Using a Stigmergy Approach in Manufacturing Coordination and Control”. In: Engineering Self-organizing Systems:Methodologies and Applications. Vol. 3464. Lecture Notes in Computer Science (LNCS). Springer, pp. 210–226.

Parunak, H. V. D., S. Brueckner, and J. Sauter (2002). “Digital Pheromone Mechanisms for Coordination of UnmannedVehicles”. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems(AAMAS’02). ACM Press, pp. 449–450.

Reitbauer, A., A. Battino, B. Saint Germain, A. Karageorgos, N. Mehandjiev, and P. Valckenaers (2004). “The Mabe Middleware:Extending Multi-Agent Systems to Enable Open Business Collaboration”. In: 6th IFIP International Conference on InformationTechnology for Balanced Automation Systems in Manufacturing and Services (BASYS). Vol. 159. Springer, pp. 53–60.

Reynolds, C. (1987). “Flocks, Herds and Schools: A Distributed Behavioral Model”. In: Proceedings of the 14th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH ’87). ACM Press, pp. 25–34.


collective intelligence - a brief...

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