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Home , Boids, Collective intelligence, Flocking (behavior), Flock (birds)

Collective a brief introduction

Gauthier Picard

MINES Saint-Étienne LaHC UMR CNRS 5516 [email protected] Preliminary: install NetLogo

http://ccl.northwestern.edu/netlogo/ Today’s Menu

What’s a Intelligence?

Some Example in the Nature

Stigmergy Colony Optimization Ant in Netlogo Social Spiders

Aggregation Behaviors (flocking) Behavior in Netlogo

Gauthier Picard 3 What’s a Collective Intelligence?

Gauthier Picard Collective Intelligence 4 Some Ideas in Bulk...

Collective, communauty Agents, subparts Convergence, common goal Mulitple interactions, Local vs. global Local , bounded rationality Simple rules Shared environment Complexity, emergent behaviors ...

Gauthier Picard Collective Intelligence 5 Today’s Menu

What’s a Collective Intelligence?

Some Example in the Nature

Stigmergy Ant Colony Optimization Ant Foraging in Netlogo Social Spiders

Aggregation Behaviors (flocking) BOIDS Flocking Behavior in Netlogo

Gauthier Picard Collective Intelligence 6 Animal Collective Intelligence , Wasps

Gauthier Picard Collective Intelligence 7 Animal Collective Intelligence Ants, Wasps

Gauthier Picard Collective Intelligence 7 Animal Collective Intelligence Termites, Humans

Gauthier Picard Collective Intelligence 8 Animal Collective Intelligence Termites, Humans

Gauthier Picard Collective Intelligence 8 Animal Collective Intelligence Crustaceans, Ants (again...)

Gauthier Picard Collective Intelligence 9 Animal Collective Intelligence ,

Gauthier Picard Collective Intelligence 10 Animal Collective Intelligence Fishes, Birds

Gauthier Picard Collective Intelligence 10 Animal Collective Intelligence Mammals

Gauthier Picard Collective Intelligence 11 Animal Collective Intelligence Mammals

Gauthier Picard Collective Intelligence 11 Gauthier Picard Collective Intelligence 12 ⇒ Models, algorithms and engineering

Artificial Collective Intelligence

How to design artificial ? How to make artificial cooperate? How to design artificial agents able to work jointly?

Gauthier Picard Collective Intelligence 13 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

Gauthier Picard Collective Intelligence 13 Bio-inspired Algorithms

Principle Taking 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 behaviors [Reynolds, 1987]

Apply to real Observe Nature Model behaviors Design algorithm problem

Gauthier Picard Collective Intelligence 14 Some Examples of Collective Behaviors

Gauthier Picard Collective Intelligence 15 Some Examples of Collective Behaviors (cont.)

Gauthier Picard Collective Intelligence 16 Some Examples of Collective Behaviors (cont.)

Gauthier Picard Collective Intelligence 17 Today’s Menu

What’s a Collective Intelligence?

Some Example in the Nature

Stigmergy Ant Colony Optimization Ant Foraging in Netlogo Social Spiders

Aggregation Behaviors (flocking) BOIDS Flocking Behavior in Netlogo

Gauthier Picard Collective Intelligence 18 Stigmergy

« The work excites the worker » [Grassé, 1959] → Behaviourist explanation indirect stimulus-responses ← Observation on termites building behaviour Consequences I Direct interactions not necessary to coordinate the work of a group I Indirect interactions are su icient I Indirect indirect between agents by the environment In social animals: termites, ants, bees, wasps, spiders, rats, etc. I Building behaviour I Recruitment I Division of labour I Prey transport I etc.

Gauthier Picard Collective Intelligence 19 Stigmergy Requirements

Stigmergy Elements I Environment I Central role I Dynamics I Individual interacting agents I Capabilities to move, perceive and act in the environment I Actions in the environment not for the others agents Stigmergy Design I Definition of the environment I What is perceived by agents I Which changes can be done by agents I What is the duration of the information: evaporation I Definition of the agents I How do they move I What they can do in the environment I In which state must they be to act: probalistic values

Gauthier Picard Collective Intelligence 20 Stigmergic Mechanisms Multi-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]

Gauthier Picard Collective Intelligence 21 Ant Algorithms [Dorigo et al., 1996] Probabilistic technique (metaheuristic) I Solving combinatorial problems I Finding good paths through graphs Stigmergic mechanism: pheromone trails I Deposited when food is found I Attracts ants (probabilistically) ↓ Evaporates when no more used (bad source) ↑ Reinforced when frequently used (good source

Gauthier Picard Collective Intelligence 22 Ant Colony Optimization (ACO)

where: k Arc Selection J , possible moves from i  τ (t)αηβ i  ij ij if j ∈ J k k P τ (t)αηβ i ηij, visibility (= 1/dij) p (t) = l∈Jk il il i,j i  k τ (t) 0 if j∈ / Ji ij , amount of pheromone on arc i,j Pheromone Deposited α β ( Q k and , parameters k Lk(t) if (i, j) ∈ T (t) k ∆ij(t) = k T (t), visited arcs at time t 0 if (i, j) ∈/ T (t) k k L (t), length of T (t) Pheromone Update Q m , parameter X k m τij(t + 1) = (1 − ρ)τij(t) + ∆ij(t) , number of ants k=1 ρ, parameter

Gauthier Picard Collective Intelligence 23 Illustration with NetLogo

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

Gauthier Picard Collective Intelligence 24 Sample Application: Collective Robotics

Gauthier Picard Collective Intelligence 25 Social Spiders (Anelosimus Eximius) [Bourjot et al., 2003] Spiders are attracted by silk and by their other congeners Several individual spiders can succeed each other to build a web

Gauthier Picard Collective Intelligence 26 Social Spiders Modeling Issue

Environment I Square grid composed of stakes with di erent heights I Initially without thread I Dynamical additions of spin threads Agents I Moving from one stake to another I Attraction by silk → contextual choice (probabilistic) of a given motion (function of the number of threads) I Putting silk at the top of a stake

Gauthier Picard Collective Intelligence 27 Social Spiders System Dynamic Coordination by Stigmergy I Implicitly modelled in the behavior I Motion influenced by silk I More there is silk in a position, and greater is the chance to be chosen I No centralisation, no social reference I Dynamic relevant to individual and social spiders

Gauthier Picard Collective Intelligence 28 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.

Social Spiders Although the grid is not well “detached” in the environment the algorithm provides good Application to Image Segmentationresults 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.

Gauthier Picard Collective Intelligence 29 Today’s Menu

What’s a Collective Intelligence?

Some Example in the Nature

Stigmergy Ant Colony Optimization Ant Foraging in Netlogo Social Spiders

Aggregation Behaviors (flocking) BOIDS Flocking Behavior in Netlogo

Gauthier Picard Collective Intelligence 30 Aggregation Behaviors (flocking)

Flock of birds, school of fish, or of 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

Gauthier Picard Collective Intelligence 31 Gauthier Picard Collective Intelligence 32 Gauthier Picard Collective Intelligence 33 Illustration avec NetLogo

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

Gauthier Picard Collective Intelligence 34 That’s all folks!

Do not hesitate to contact me: [email protected]

Gauthier Picard Collective Intelligence 35 References

Armetta, F., S. Hassas, S. Pimont, and E. Gonon (2004). “Managing Dynamic Flows in Production Chains Through Self-”. In: Engineering Self-Organising Systems: and Applications. Vol. 3464. Lecture Notes in (LNCS). Springer, pp. 240–255. Bourjot, C., V. Chevrier, and V. Thomas (2003). “A New Swarm Mechanism based on Social Spiders Colonies : from Web Weaving 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 of Computer 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 Engineering Self-Organising Applications Workshop, ESOA 2003, held at AAMAS 2003 in Melbourne, Australia, in July 2003 and selected invited 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 Networks”. In: Proceedings of the 5th International Conference on Parallel from Nature (PPSN V). Lecture Notes in Computer Science (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: IEEE Transactions on Systems, Man, and Part B: Cybernetics 26.1, pp. 29–41. Foukia, N. and S. Hassas (2004). “Managing Computer Networks Security through Self-Organization: A Perspective”. In: Engineering Self-Organising Systems, Nature-Inspired Approaches to Software Engineering. Vol. 2977. Lecture Notes in Computer Science (LNCS). Springer, pp. 124–138.

Gauthier Picard Collective Intelligence 36 References (cont.)

Grassé, P. (1959). “La reconstruction du nid et les interactions inter-individuelles chez les bellicositermes natalenis et cubitermes 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). “Emergent Forecasting 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 Unmanned Vehicles”. 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 ”. In: 6th IFIP International Conference on Information Technology for Balanced Automation Systems in Manufacturing and Services (BASYS). Vol. 159. Springer, pp. 53–60. Reynolds, C. (1987). “Flocks, and Schools: A Distributed Behavioral Model”. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’87). ACM Press, pp. 25–34.

Gauthier Picard Collective Intelligence 37