Engineering Self-Organizing Systems Cognition & Emergence of Control

1. Engineering Self-Organizing SystemsCognition & Emergence of Control Salima Hassas University of Lyon

2. Summary • From Organization to Self-Organization • Organization dynamics & self-organization • Relation entre Organization/Control • Engineering (Self)–organization in MAS • Organization oriented approaches • Dynamic Organization et re-Organization • Self-organization • Engineering Self-Organization: a complex system perspective for cognition • Organization as emergent control system • Cognition, representation and evolution • Conclusions

3. (Self) Organization • What is an organization?A non-random arrangement of components or parts interconnected in a manner as to constitute a system identifiable as a unit.

4. (Self) Organization • What is an organization?A non-random arrangement of components or parts interconnected in a manner as to constitute a system identifiable as a unit.  Existence of a process that produces the organization

5. Different situations of (Self) Organization Process Dynamic Internal Static Dynamic external Static Organization (a priori) Organization a posteriori a priori Static Dynamic Static Dynamic

6. (Self) Organization • System’s organization is static, defined a priori • predefined roles, relations • Process producing it, external to the organization, known and defined a priori  The organization is static all along the system’s life : no change/no adaptation

7. Engineering (Self) Organization • In Multi-Agents Systems • Organization based methodologies/tools: Ex: AGR, TAEMS,MOISE+, ..etc • System dynamics and Organization explicitly specified at Design time. Ferber &al.  No adaptation (even programmed)

8. (Self) Organization Process Dynamic Internal Static Dynamic Dynamic Organization external Static Organization Organization a posteriori a priori Static Dynamic Static Dynamic

9. (Self) Organization Dynamic (re) Organization • System’s organization is dynamic, but known a priori • Characteristics provided • The process producing it, is external to the organization, but is conditioned by the environment • Ex: Agents dynamically organize themselves to form a circle circle • The organization is dynamic • Programmed re-organization in order to adapt to the environment constraints

10. (Self) Organization Dynamic (re) Organization Example of rule: Heterarchy if #agents<= n, Hierarchy if #agents > n • The organization is dynamic • Programmed re-organization/adaption environment

11. (Self) Organization Engineering Dynamic (re) Organization • Use of Meta-Organizations + transformation rules, Observer/controler based architectures, rule based organization process, etc. • At design-time  Specify interactions rules, transformation rules, observation/control architecture Example : Specify transformation rules : Heterarchy  Hierarchy • If (condition) then select n agents, elect a leader, etc. • At run-time  Generate organization according to the specified conditions changes

12. (Self) Organization Process Dynamic Internal Emergent Organization & static control Static Dynamic Dynamic Organization external Static Organization Organization a posteriori a priori Static Dynamic Static Dynamic

13. (Weak) Self Organization • System’s organization is dynamic, and unknown a priori (emergent) • Environment constraints provided • The process producing it, internal to the organization, and is conditioned by the environment • System behavior/coupled with the environment change/constraints • The organization is emergent : System/environment coupling is a complex program • Program the system-environment coupling • (ex: bio-inspired techniques, game theory, evolutionary control)

14. Engineering (Weak) Self Organization AMAS theory (Game theory inspiration) – (P. Glize, MP. Gleize, & al.) • Basic Principle (Axelrod’s work on iterated games) => Long term perspective : Altruist strategy always wins (Cooperative attitude) - Design time • Define cooperative/non cooperative situations • Define rules to pass from coop/non coop - Run time • The system finds by itself the adequate organization to solve the problem (the organization is not explicit) • A kind of « Situation-based » programmed adaptation (http://www.irit.fr/ADELFE)

15. Engineering (Weak) Self Organization Social insects Inspiration (ants foraging, collective sorting, ..) • Case 1: Transposing metaphors (mimic biological systems) • Routing Algorithms in Networks , ACO meta heuristic, ..etc. • Routing (Rare) Information in P2P networks (illustration) Biology Biological Model Induction Observations

16. Engineering (Weak) Self Organization Social insects Inspiration (ants foraging, collective sorting, ..) • Case 1: Transposing metaphors (mimic biological systems) • Routing Algorithms in Networks , ACO meta heuristic, ..etc. • Routing (Rare) Information in P2P networks (illustration) Biology Simulation Computing Biological Model Computation Model Induction Observations

17. Engineering (Weak) Self Organization Social insects Inspiration (ants foraging, collective sorting, ..) • Case 1: Transposing metaphors (mimic biological systems) • Routing Algorithms in Networks , ACO meta heuristic, ..etc. • Routing (Rare) Information in P2P networks (Illustration) Biology Computing Simulation Biological Model Computation Model Transposing Induction Observations Applications

18. Engineering (Weak) Self Organization Social insects Inspiration (ants foraging, collective sorting, ..) • Case 2: more deepened understanding • Stigmergy in Negotiation : CESNA - Exchange between Stigmergic Negotiating Agents- (Armetta & Hassas 2006) • Environment Pressure selection +structural coupling • Work of L. Steels – Emergence of language • Coupling structure/behaviors (retro-active co-evolution of social and spatial organizations in MAS) (Illustration on: ants foraging) • Application on the web: Social Tagging, social networks (MySurf, UTTU) • Case of neuronal computing + evolutionary algorithm • Selection: evolutionary algorithm • Structural coupling: change in neural networks

19. (Self) Organization Process Dynamic Emergent Organization & emergent control Internal Emergent Organization & dynamic (programmed) control Static Dynamic Dynamic Organization & static programmed control external Static Organization Organization a posteriori a priori Static Dynamic Static Dynamic

20. (Strong) Self Organization • System’s organization is dynamic, and unknown a priori (emergent) • Environment constraints provided • The process producing it, internal to the organization, and produced by system/environment dynamics coupling • System organization and behavior/environment change constraints strongly coupled in a retro-active loop(ex: natural ants foraging) • The organization and the process are emergent : strong coupling of system/environment dynamics

21. Engineering (Strong) Self Organization • The organization and the process are emergent : strong coupling of system/environment • System/environment coupling is produced by the system/environment dynamics • Need for (system) cognition and evolution

22. Multi-Agents System = Collectif of situated agents in a shared un environnement • Agents are des local and autonomous units of material symbols processing • Agents inspired by human societies, but can represent : neurons, birds, fish, cells, particles, etc. Collective Individual External Internal Ferber, J. (1995). Les systèmes multi-agents. Vers une intelligence collective. Paris: InterEditions. 22

23. MAS Analysis according to 4 quadrants (J. Ferber 2006) Ferber, J. (2006). Concepts et méthodologies multi-agents. In F. Amblard, & D. Phan (Ed), Modélisation et simulation multi-agents : applications pour les Sciences de l'Homme et de la Société (pp. 23-48). Paris: Lavoisier.

24. Shapes Fix the solution => memory Shapes Second Order Praxis First Order Praxis Parameters Change • MER from a collective/Internal composition • (L. Lana de Carvalho & al. ECCS’2008) Exploring Complex System Exchange of shared elements Complex System Adaptive Exploiting Emerges Emerges Representation Behaviors Problem Environment emergence Praxis (collective action) In evolution Parameters CAS*AGENT AGENT*AGENT Individual/External Indivodual/Internal Collective/Internal Collective/External

25. Engineering (Strong) Self Organization • The organization and the process are emergent : strong coupling of system/environment • System/environment coupling is produced by the system/environment dynamics • Need for (system) cognition and evolution • Self-organization= emergence of a new system that controls the initial system (to organize)

26. Conclusion Process Organization= Emergent control system Dynamic Organization= Emergent from a dynamics as a control Internal Organization= Result of a programmed control Static Dynamic Organization= Result of designed fixed control external Organization a posteriori a priori Static Dynamic Static Dynamic

27. Dynamical Approach • Dynamic Representations • Representations part of the cognitive development structure • Cognitivism • Material Representations • Logics Theorms • Complex Systems Approach • Cognition & Representations : • Complex Systems • Representations are immerged (stables & non-reactive) • Multi-Agents Systems Different Approaches for Cognition Evolution towards Complex Systems • Enactivism • Self-organization • Natural tendency • Embodied Cognition • Emergence • Connexionism • Micro-representations • Macro-representations • Neural Networks Mitchell, M. (1998) Steels, L. (2003) Rocha, L. M. & Hordijk, W. (2005) Carvalho, L. L. & Hassas, S. (2005, 2008) Thelen, E. & Smith, T. B. (1993) van Gelder, T. & Port, R. F. (1995) Maturana, H. & Varela, F. J. (1973) Varela, F. J., Thompson, E. & Rosch, E. (1991) McCulloch, W. S. & Pitts, W. (1943) Rosenblatt, F. (1962) Rumelhart, D. E. & Norman, D. A. (1981) Newell & Simon, H. A. (1976) Fodor, J. A. (1983) Turing, A. (1936) Date d’apparition

28. Approche Systèmes Complexes de la Cognition • Pour quoi les représentations sont importantes en psychologie ? A’ B • Les représentations instancient l’acte intentionnel • L’auto-organisation n’assure pas à un système complexe une forme optimale. • L’auto-organisation n’arrive pas seule à guider le développement cognitif des organismes complexes. Stance Intentionnelle Représentations Emergentes Auto- Développement Stance de Design Auto-Organisation Auto-Adaptation Stance Physique Modèles équationnels, point de vue classique en sciences A A f ( c, a) B C C f ( c, a) Systèmes Complexes ↑ Interaction de Fonctions Simples Systèmes Cognitifs ↑ Réactivité Brisée Spontanément Représentations Emergentes : une Approche Multi-Agents des Systèmes Complexes Adaptifs en Psychologie Cognitive