Self-organization in Multi-Agent Systems

Self-organization

Self-organization
• The term self-organization occurs in many branches of science.
• Researchers study existing phenomena of self-organization, but they take different viewpoints and analyze different aspects (see [1–4]). This makes self-organization a very
interdisciplinary and heterogeneous field.

Self-organization in Multi-Agent System
• We regard a system consisting of several entities. We call this system organized if it has a certain structure and
functionality [4].

Self-organization in Multi-Agent System
• We regard a system consisting of several entities. We call this system organized if it has a certain structure and
functionality [4].
• Structure means that the entities are arranged in a particular manner and interact (communicate) with each other in some way.

Self-organization in Multi-Agent System
• We regard a system consisting of several entities. We call this system organized if it has a certain structure and functionality [4].
• Functionality means that the overall system fulfills a certain purpose.
– For example, a school of small fish tries to achieve a group structure that protects the fish against enemies.

Self-organization in Multi-Agent System
• A system is self-organized if it is organized without any
external or central dedicated control entity.
• The individual entities interact directly with each other in a distributed peer-to-peer fashion. Interaction between the entities is usually localized.
• For example, in a school of fish each individual fish bases its behavior on its observation of the position and speed of its immediate neighbors, rather than on the behavior of a
“central fish” or that of the whole school.

Self-organization in Multi-Agent System
• Self-organization is also about the relationship between the
behavior of the individual entities (the microscopic level) and the resulting structure and functionality of
the overall system (the macroscopic level).
• In self-organized systems, the application of rather simple
behavior at the microscopic level leads to sophisticated
organization of the overall system. This phenomenon is called emergent behavior.
• Why and how emergent behavior occurs is still not well understood.

Motivation
• Operate in dynamic, heterogeneous environments and face the challenge of handling frequently changing
requirements. Such ubiquitous computing, biological systems and autonomic computing.
• Must be scalable, robust and capable of adapting to the circumstances.
• The ability of a system to have a complex collective
response arising from interactions among relatively
simple individual components.

Multi-agent System
• Autonomous software entites are agents
• MAS is a group of cooperating agents
• Macroscopic behaviour using only locally agents interations

Self-Organization - Definitions
• Defined as the mechanism or the process enabling a system to change its organization without explicit external
command during its execution time.
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Self-Organization - Definitions
• Defined as the mechanism or the process enabling a system to change its organization without explicit external
command during its execution time.
Strong self-organizing systems are those systems where there is no explicit central control either internal or external.
Purely decentralized (access to global information is limited or impossible)
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Self-Organization - Definitions
• Defined as the mechanism or the process enabling a system to change its organization without explicit external
command during its execution time.
Weak self-organizing system are those systems where, from an internal point of view, there is re-organization maybe under an internal (central) control or planning.
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Self-Organization - Definitions
• Defined as the mechanism or the process enabling a system to change its organization without explicit external
command during its execution time.
Weak self-organizing system are those systems where, from an internal point of view, there is re-organization maybe under an internal (central) control or planning.
• Interactions occur locally (among neighbours) and based on local information.
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Self-Organization – Properties
Absence of explicit external control.
• The system is autonomous;
• It imposes and changes its organization based solely on
internal decisions and without following any explicit external (re-) organization command.
• This is a mandatory property
• This property refers to the self- part of the self-organization definition.

Self-Organization – Properties
Decentralized control.
• A self-organizing system can work under decentralized control.
• In this case, there is no internal central authority or
centralized information flow.
• As a result, access to global information is limited by the locality of interactions, which is governed by simple rules.
• This property is generally not mandatory, since we can observe internal central control in many natural selforganizing systems, such as the termite systems.

Self-Organization – Properties
Dynamic operation.
• This mandatory property is linked to the system evolution
in time.
• Since the organization evolves independently of any external control, this property implies continuity in the self-organization process.

Self-Organization – Characteristics
• Endogenous global order.
– The system reaches some (stable) global state that is produced from
within the system.
• Emergence.
– This refers to having emergent phenomena arising from local interactions. Such phenomena can be observed at global level but they cannot be accurately deduced from examining individual behaviors.
Arise from local interactions occurring among the individual components.
• Local rules.
– The overall complex system behavior can be based on simple
individual behavioral rules. Local information describes the mechanism for producing the global behavioral pattern and not the pattern itself.

Self-Organization – Characteristics
• Dissipation.
– In the absence of external perturbation, the system is expected to
stabilize in some states in which emergent properties can be observed. This implies a kind of dissipation of some ‘ energy ’ , otherwise the system would be continuously changing.
• Instability.
– Systems showing instability are mainly characterized by nonlinear dynamics which have the effect of small fluctuations in
environmental conditions that result in significant variations in overall system behavior.
• Multiple equilibria.
– Multiple equilibria are observed when many possible attractors for
stable states are present in the system.

Self-Organization – Characteristics
• Self-maintenance.
– A system is self-maintained if it has the capacity to reproduce or
repair itself, essentially by reproducing or repairing its components.
• Adaptivity.
– The re-organization capability of self-organizing systems implies
adaptation to external (environmental) variations.
• Complexity.
– This characteristic usually arises from the irreducibility of the global properties to a combination of local behaviors.
• Hierarchies.
– Hierarchies are present in a system when multiple nested self-
organized levels can be observed.

Self-Organization - Measures
Typical self-organization measures includes:
• Capacity to reach an organization able to fulfil the goal of the system as a whole once the system is started
(success/failure/time required, convergence);
• Capacity to reach a re-organization after a pertubing event (success/failure/time required);
• Degree of decentralized control (central/totally decentralized/hybrid);
• Capacity to withstand pertubations
(stability/adaptability)

Emergence

Emergence - Definitions
•
The whole is more than the sum of the parts
. Witch captures the essence of the emergent phenomena.
• Di Marzo Serugendo el at., 2006:
A structure (pattern, property or function), not explicitly represented at the level of the individual components (lower level), and which
appears at the level of the system (higher level).
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Pheromone Path of Ants
• What does emerge?
• It is considered emergent if and only if there exists an observer capable of observing and characterizing it as such.
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Emergence – Properties
• Novelty.
– Refers to the fact that although the resulting phenomenon is derived from a particular organization and interaction of the micro-level parts, it is radically different from their
individual properties and it cannot be derived or predicted from them.
• Irreducibility.
– Structures and/or functions appear at a macroscopic level and the observation of the component properties cannot enable to predict them (Atlan, 1993). For example, in complex systems, the complexity often results in features, emergent properties, which are properties of the system that the individual parts do not have.

Emergence – Properties
• Interdependency between levels.
– In these systems there are at least two levels, a micro-level corresponding to the substrate where the emergent phenomena comes from and a macro-level enabling the observation of the emergent phenomena. The micro-level
causes the emergent phenomena and the macro-level
constrains the behavior of the entities at the micro-level.
Therefore, there is a strong dependency between the dynamics observed at both macro- and micro-levels.

Why Decentralized?
• Computation and decisions to be distributed among the different components, thus preventing the need for a
central powerful computer;
• The system is more robust since it does not rely on a single node that may fail and crash the whole system;

Why Decentralized?
• Network and CPU resources are better used in the sense that communication does not occur among a dedicated
central node and a large number of components, but locally among the whole set of components;
• In dynamic systems, where components join and leave the system permanently, decentralized control allows a flexible schema for communication, e.g. with a neighbor instead of with the central entity.

Examples
• Natural self-organizing systems include well-known examples concerning social insects, such as ants, termites and honey bees.
– Communication occurs through stigmergy by the means of pheromone deposited into their environment.

Examples
• Other collective behaviors of animals referred to as selforganizing are flocks of birds, and schools of fish.
– By following simple rules, such as getting close to a similar
bird (or fish) but not too much, getting away from
dissimilar birds (or fishes), they are able to collectively avoid predators.

Examples
• Among artificial multi-agent based self-organizing systems, we observe different trends ranging from application of naturally-inspired self-organizing models, to the establishment of new mechanisms and whole infrastructures supporting self-organization of artificial systems.
– Swarms provide a great source of inspiration, especially for fixed and mobile networks systems management [11] , such as routing, load balancing [43] or security [25] .
– At the level of whole infrastructures (middleware) supporting artificial self-organizing systems, some works take their inspiration from magnetic fields [40] , or ants [2] .

Application to a Self-Organizing
• Simple stigmergy-based system, where components
communicate indirectly with each other by modifying
their local environment, by inserting virtual tags at their local positions.

Application to a Self-Organizing
• Simple stigmergy-based system, where components communicate indirectly with each other by modifying their local environment, by inserting virtual tags at their local positions.
• An example could be given by traffic lights controllers inserting traffic flow information observed at the 8 branches of a road intersection (4 paths and 2 directions for each path). Cars arriving at road intersections sense the traffic flow and may decide to change their route.

Application to a Self-Organizing
• The components here are:
– the different mobile entities (cars);
– the traffic light controllers.
– Cars are programmed to go from one source location to a destination location and are equipped with an initial planned route.

Application to a Self-Organizing
• The metadata here is given by the traffic flow (8 values) at each road intersection. These values are permanently updated by the traffic light controllers sitting at each road intersection.

Application to a Self-Organizing
• Some examples of policies could be:
– Cars-Policy: if traffic flow down the path is above threshold, change direction and recalculate route.
– TrafficLights-Policy: modify traffic lights duration according to traffic flow observed.
– Coordination-Policy: remove out-dated information.

Application to a Self-Organizing
• Some examples of policies could be:
– Cars-Policy: if traffic flow down the path is above threshold, change direction and recalculate route.
– TrafficLights-Policy: modify traffic lights duration ac- cording to traffic flow observed.
– Coordination-Policy: remove out-dated information.
• This is an example with several independent components communicating indirectly and taking decisions on the basis of locally available metadata.

References
• Gardelli, Luca and Viroli, Mirko and Omicini, Andrea. "Design Patterns for Self-Organizing
Multiagent Systems ” , 2nd International Workshop on Engineering Emergence in Decentralised
Autonomic Systems (EEDAS 2007), CMS Press, University of Greenwich, London, UK. 61-70.
• G. Di Marzo Serugendo, M.-P. Gleizes, A. Karageorgos. "Self-organisation and emergence in
MAS: an overview", Informatica 30(1): 45-54, Slovene Society Informatika, Ljubljana, Slovenia,
2006.
• G. Di Marzo Serugendo, M.-P. Gleizes, A. Karageorgos. "Self-Organisation in MAS", Knowledge
Engineering Review 20(2):165-189, Cambridge University Press, 2005.
• G. Di Marzo Serugendo. "Autonomous Systems with Emergent Behaviour". Chapter in
“Handbook of Research on Nature Inspired Computing for Economy and Management”. Jean-Philippe
Rennard (Ed), Idea Group, Inc., Hershey-PA, USA, pp. 429-443, September 2006.
• T. De Wolf, Analysing and engineering self-organising emergent applications, Ph.D. Thesis,
Department of Computer Science, K.U.Leuven, Leuven, Belgium, May, 2007.
• T. De Wolf, and T. Holvoet, Emergence Versus Self-Organisation: Different Concepts but
Promising When Combined, Engineering Self Organising Systems: Methodologies and
Applications (Brueckner, S. and Di Marzo Serugendo, G. and Karageorgos, A. and Nagpal, R., eds.),
Lecture Notes in Computer Science, 2005, Volume 3464, May 2005.
• T. De Wolf, and T. Holvoet, Design Patterns for Decentralised Coordination in Self-organising
Emergent Systems, Editors: Sven Brueckner, Salima Hassas, Màrk Jelasity and Daniel Yamins,
Engineering Self-Organising Systems: Fourth International Workshop, ESOA 2006, Future
University-Hakodate, Japan, 2006, Revised Selected Papers, Lecture Notes in Computer Science,
Volume 4335, 2007, pp. 28–49, Springer Verlag
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References
• Jan Sudeikat and Wolfgang Renz: "Toward Requirements Engineering for Self-Organizing
Multi-Agent Systems" in: Proceedings of the First International Conference on Self-Adaptive and
Self-Organizing Systems, 2007.
• Holland, J. H. (1998). Emergence – from Chaos to Order. Oxford University Press.

The End.
Questions?