FRE 2672
TFG Self-Organization - 01/07/2004
Engineering Self-Organization in MAS
Complex adaptive systems using situated MAS
Salima Hassas
LIRIS-CNRS
Lyon I University, France
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Outline
Introduction
Self-organizing Computing systems as self-organizing Situated MAS
Coupling to the environmentCo-evolution of social and spatial organizationsA complex adaptive system perspective
Some illustrations
Conclusion
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Introduction
Evolution of computing systems: High complexityComplex environments, sophisticated applicationsComplex data and usages/practices;Emergence of new needs, new practices, …
Computing system: a system open on its environment
Complexity of the environment: distributed, dynamic, evolving, uncertain, …
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Examples
Evolution of the Internet and the WebA complex dynamic network, exhibiting a self-organizing
character…
Evolution of Software EngineeringAwareness of dynamic changes of the environmentDesign at run-time
User more and more present User centered systemsCapture usages/practices through system use
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Issue
How to design computing systems exhibiting intelligence while embodied in their environment,
considered at its widest meaning?
Widest meaning:
physical as well as conceptual environment
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Issue
Environment is thus put at the heart of the engineering of the computing system
Conceptual environment related to uses (practices)Place of materialization of uses
(ex: Virtual communities)
Physical environmentPlace of materialization (embodiment) of the computing
system =>a complex network of resources
Place of inscription of traces of uses related to actions and interactions
=> Ex: web/Internet topology expresses usages
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Approach
The system is considered through its coupling with its environment
A double articulation:Physical articulation : Structural Coupling Conceptual articulation : Behavioral Coupling
Retroactive effects of one coupling on another Organizational articulation
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Implications on a MAS
Using Situated MAS to implement this kind of computing systems (complex systems aware of their environment)
The MAS is subject to the same coupling with its environment:
Structural Coupling : Physical articulationSpatial organization of the MAS / physical environment
Behavioral Coupling : Conceptual articulationSocial organization of the MAS / conceptual environment
Retroactive effects of one coupling on another Co-evolution of spatial and social organizations of the MAS
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Implications on a MAS
The design of the situated MAS must address
its spatial organization its social organization And the co-evolution of both organizations through the MAS dynamics
Self-organization is mandatory The eternal ants foraging example
Emergent Structures : shortest paths from nest to food sourcePhysical materialization of the spatial organization
Emergent behavior : self-catalytic frequentation of paths Conceptual materialization of the social organization
Self-organization is the mechanism which allows co-evolution of social and spatial organization
Need for a “glue” between both organizations : stigmergy mechanism
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Our vision
The computing system as a Complex Adaptive System A set of interconnected components (agents), strongly
interacting with one another at different levels :
Micro level: retroactive interactions between agents (local behaviors)
Macro level: emerging structure and organization of the system(global behaviors)
System Dynamics : maintaining the system organizationNon linear dynamic (retroactions and emergences)
Coupling to the environment: autopoïetic vision Co-evolution “structures-their generating processes”:
reflective loopCo-evolution of “spatial organization-social organization” in MAS
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Positioning
Self-Organizing Computing systems
Complex Adaptive Systems
Non Linear DynamicSystems
Situated Multi-Agents
General System TheoryCybernetics
Chaos Theory, statistical mechanics,..
Artificial lifeEmbodied intelligenceNature-inspired computing
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Propositions
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A Guiding framework
A framework for developing self-organizing computing systems:
Physical materialization of the environment and its spatial representation
A complex dynamic network of resources: importance of topology
Embodied Intelligence using situated agents Population of situated agents embodied in a physical (spatial) environment ==> incarnation of the computing system
StigmergySpatial structure for coding control and meta-control information
case of the electronic pheromone
Individual behaviors CorrelationStrategy: balancing exploitation (reinforcement) /exploration (diversity)
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Topology
Scale Free Networks and « small world » property
Scale freeSmall number of highly
connected nodes, distributed randomly
High number of nodes weakly connected
Small worldSmall average length between
any couple of nodes
Topology of networks produced by human activities / nature (exhibiting self-organization ..)
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Illustration
A computing ecosystem on the web: WACO system
A multi-agents system : An ecosystem composed of Web Ants (mobile agents),
mapped on the webUsing a social insects paradigm (stigmergy)
Combining foraging and collective sorting
Specialization/population regulation following the web content
Dynamics of population: Energy mechanism (order/disorder of web content)
(IEEE Swarm Intelligence 03 publication)
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Illustration
Experiment1 : Disorder decreasing
Disorder decreases while new
documents are created Disorder=number of scattered
documents Negative value of disorder : multiple
clustering of a same document
Scattered documents are those created(order emergence)
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Illustration
Experiment 2 : Clusters forming
Effectiveness of clustering Size of clusters increases regularly Sudden (small) decrease of mean
clusters sizes near time 80000Order emergence disturbed by new creations
Scattered documents are those created
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Illustration
Experiment3: Energy evolution
Energy evolution follows the disorder evolution Decrease near time 80000
=> order emergence
Decrease near time 100000 => new clustering operation:
specialists creation
order emergence
Specialists creation
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Illustration
Energy of specialized agents
Specialists energy increase during clusters forming
Near order emergence (near time 80000) energy = 0
Sudden increasing near time 100000, new clusters apparition
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Illustration
ECoNET Dynamic multi-criteria balancing on a network of processorsProblem:
On a network of processors, processes must find dynamically a spatial repartition allowing the satisfaction of the 4 following criteria :
Balancing the average of the perceived load
Spatial clustering of processes belonging to the same application (sharing of same data, resources)
Spatial clustering of processes belonging to highly communicating different applications (minimize communications delays)
Spatial repulsion of concurrent processes accessing the same resources (resources access conflicts)
Note
Environment is subject to perturbations and criteria may evolve during time..
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Conclusion
Towards a methodolgy of self-organizing computing systems
Environment : A central point for the systemSituated MAS paradigm: incarnation of the computing
system The MAS is subject to the same coupling with respect to its
environment
Deployment of the MAS in its physical environment : spatial organization
Maintaining the spatial organization through the social organization of the MAS
Retro-active effects of one organization on the other
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Conclusion
Necessary to study :Relation between spatial organization and the
environment topology (and their retro-active effects)Reflective coupling: structure-processes (autopoïesis)
Relation between spatial organization and social organization (and their retro-active effects)
Structure-environment coupling (self-organization)
Reflective effects between the two coupling Co-evolution of both (emergent) organizations and the
environment topology
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Thank you :))