Engineering Self-Organizing SystemsCognition &
Emergence of Control
Salima HassasUniversity of Lyon
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
(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.
(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
Different situations of (Self) Organization
a priori
Static Organization (a priori)
Process
Organization
Inte
rnal
ex
tern
al
DynamicStatic Dynamic Static a posteriori
Dynamic
Dynamic
Static
• 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
• In Multi-Agents SystemsOrganization based
methodologies/tools:
Ex: AGR, TAEMS,MOISE+, ..etc
System dynamics and Organization explicitly specified at Design time.
No adaptation (even programmed)
Ferber &al.
(Self) Organization
a priori
Static Organization
Process
Organization
Inte
rnal
ex
tern
al
DynamicStatic Dynamic Static a posteriori
Dynamic
Dynamic
Static
Dynamic 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
The organization is dynamic Programmed re-organization in order to adapt to the environment constraints
circlecircle
The organization is dynamic Programmed re-organization/adaption environment
Example of rule: Heterarchy if #agents<= n, Hierarchy if #agents > n
• 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
architectureExample : 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
(Self) Organization
a priori
Static Organization
Process
Organization
Inte
rnal
ex
tern
al
DynamicStatic Dynamic Static a posteriori
Dynamic
Dynamic
Static
Dynamic Organization
Emergent Organization& static control
• 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)
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)
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)
ObservationsObservations
Biological Model
Biological Model
Induction
Biology
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)
ObservationsObservations
Biological Model
Biological Model
Induction
Biology
Computation Model
Computation Model
Computing
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)
ObservationsObservations
Biological Model
Biological Model
Induction
Biology
ApplicationsApplications
Computation Model
Computation Model
Transposing
Computing
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
(Self) Organization
a priori
Static Organization
Process
Organization
Inte
rnal
ex
tern
al
DynamicStatic Dynamic Static a posteriori
Dynamic
Dynamic
Static
Dynamic Organization & static programmed control
Emergent Organization& dynamic (programmed) control
Emergent Organization & emergent control
• 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
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
Multi-Agents System = Collectif of situated agents in a shared un environnement
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Ferber, J. (1995). Les systèmes multi-agents. Vers une intelligence collective. Paris: InterEditions.
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.
IndividualCollective
InternalExternal
Individual/internal Individual/external
MeSubjectivity
He,She, ThisObjectivity
<Mental states, Agents architectures>
<Agents behavior>
Interiority
The object
Collective/internal Collective/external
WeIntersubjectivity
Them, all thisInterobjectivity
<Interactions modes, Shared knowledge><Organizations, institutions,Evolution/ organizations emergence, social facts>
the noosphere The social structure
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.
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Internal to Agent External to Agent
MAS Analysis according to 4 quadrants (J. Ferber 2006)
MER from a collective/Internal composition(L. Lana de Carvalho & al. ECCS’2008)
Exploring Complex System
Problem Environment
Parameters Change
Shapes
Shapes
Second Order Praxis
Agents
In evolution
Fix the solution => memory
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AGENT*AGENTCAS*AGENT
Parameters
Indivodual/Internal
(collective action)Praxis
Individual/External
Complex System
Collective/External
emergenceBehaviors
Collective/Internal
RepresentationAdaptiveExploiting
First Order Praxis
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)
Conclusion
a priori
Organization=Result of
designed fixed control
Process
Organization
Inte
rnal
ex
tern
al
DynamicStatic Dynamic Static a posteriori
Dynamic
Dynamic
StaticOrganization=
Result of a programmed
control
Organization=Emergent from a
dynamics as a control
Organization=Emergent control
system
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Date d’apparition
Cognitivism• Material Representations• Logics Theorms
Dynamical Approach• Dynamic Representations• Representations part of the
cognitive development structure
Connexionism• Micro-representations• Macro-representations• Neural Networks
Enactivism• Self-organization• Natural tendency• Embodied Cognition• Emergence
Complex Systems Approach• Cognition & Representations :
Complex Systems• Representations are immerged (stables
& non-reactive)• Multi-Agents Systems
Different Approaches for CognitionDifferent Approaches for Cognition
Turing, A. (1936) Newell & Simon, H. A. (1976) Fodor, J. A. (1983)
McCulloch, W. S. & Pitts, W. (1943)Rosenblatt, F. (1962)Rumelhart, D. E. & Norman, D. A. (1981)
Maturana, H. & Varela, F. J. (1973)Varela, F. J., Thompson, E. & Rosch, E. (1991)
van Gelder, T. & Port, R. F. (1995)Thelen, E. & Smith, T. B. (1993)
Mitchell, M. (1998) Steels, L. (2003)Rocha, L. M. & Hordijk, W. (2005)
Carvalho, L. L. & Hassas, S. (2005, 2008)
Systèmes Complexes
↑Interaction de
Fonctions Simples
Systèmes Cognitifs↑
Réactivité Brisée Spontanément
f ( c, a)
Stance Physique
Modèles équationnels, point de vue classique en sciences
C
A
C
B
AB
A’
Stance de Design
Auto-OrganisationAuto-Adaptation
Stance Intentionnelle
Représentations EmergentesAuto- Développement
Pour quoi les représentations sont importantes en psychologie ?
• 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.
f ( c, a)
Approche Systèmes Complexes de la Cognition
Représentations Emergentes : une Approche Multi-Agents des Systèmes Complexes Adaptifs en Psychologie Cognitive 28