Self-organisation and emergence in MAS: an overview.
The spread of the Internet and the evolution of mobile communication, have created new possibilities for software applications such as ubiquitous computing, dynamic supply chains and medical home care. Such systems need to operate in dynamic, heterogeneous environments and face the challenge of handling frequently changing requirements; therefore they must be flexible, robust and capable of adapting to the circumstances. It is widely believed that multi-agent systems coordinated by self-organisation and emergence mechanisms are an effective way to design these systems. This paper aims to define the concepts of self-organisation and emergence and to provide a state of the art survey about the different classes of self-organisation mechanisms applied in the multi-agent systems domain. Furthermore, the strengths and limits of these approaches are examined and research issues are provided.
Povzetek: Clanek opisuje pregled samoorganizacije v MAS.
Keywords: multi-agent systems, self-organisation, emergence
Natural self-organising systems function without central control and operate based on contextual local interactions. The particularity par·tic·u·lar·i·ty
n. pl. par·tic·u·lar·i·ties
1. The quality or state of being particular rather than general.
2. of self-organised systems is their capacity to spontaneously (without external control) produce a new organisation in case of environmental changes. These systems are particularly robust, because they adapt to these changes, and are able to ensure their own survivability sur·viv·a·ble
1. Capable of surviving: survivable organisms in a hostile environment.
2. That can be survived: a survivable, but very serious, illness. . In some cases, self-organisation is coupled with emergent behaviour, in the sense that although individual components carry out a simple task, as a whole they are able to carry out complex tasks emerging in a coherent way through the local interactions of the various components.
The complexity of today's applications is such, e.g. world scale, that no centralised or hierarchical control is possible. In other cases, it is the unforeseeable Un`fore`see´a`ble
a. 1. Incapable of being foreseen.
Adj. 1. unforeseeable - incapable of being anticipated; "unforeseeable consequences"
unpredictable - not capable of being foretold
context, in which the application evolves or moves, which makes any supervision difficult. Therefore, we are witnessing an increased interest from both the academic community and the industry in naturally inspired (robust and simple) solutions for building modern applications favouring self-organisation and/or emergence of properties.
We can foresee that among the applications of tomorrow, a great many of them will be biologically inspired: self-organising sensors networks, allowing the control of aerospace vehicles, or of dangerous zones; self-organising traffic management, allowing re-routing of emergency vehicles, or individual cars; storage facilities, or self-managing operating systems facilities. Some others applications tackle with complex problem solving problem solving
Process involved in finding a solution to a problem. Many animals routinely solve problems of locomotion, food finding, and shelter through trial and error. in which complexity is due to the great space search such as optimisation problems and non linear problems.
Software agents naturally play the role of autonomous entities subject to self-organise themselves. Usually agents are used for simulating self-organising systems, in order to better understand or establish models. The tendency is now to shift the role of agents from simulation to the development of distributed systems where components are software agents that once deployed in a given environment self-organise and work in a decentralised manner towards the realisation of a given (global) possibly emergent functionality.
Sections 2 and 3 review the notions of self-organisation and emergence respectively. Section 4 provides the description of several implementations in MAS. Section 5 discusses the strengths and limits of self-organising approaches. The main problems and challenges related to the software engineering of self-organising systems which exhibit emergent properties are discussed in Section 6. Finally, Section 7 concludes the paper.
By studying the social behaviour of insects (termites), Grasse  proposed in 1959 the theory of stigmergy, which can be summarised in "the work excites the workers". The consequence is that direct interactions are not necessary to coordinate a group, for example indirect communications through environment are enough. Coordination and regulation tasks are realised on the basis of information deposited into the environment, without central control. In the case of ants and termites, stigmergy is ensured by depositing a chemical substance in the environment, called pheromone pheromone
Any chemical compound secreted by an organism in minute amounts to elicit a particular reaction from other organisms of the same species. Pheromones are widespread among insects and vertebrates (except birds) and are present in some fungi, slime molds, and algae. .
In the 70es, the term self-organisation itself has been established by Nobel Prize Nobel Prize, award given for outstanding achievement in physics, chemistry, physiology or medicine, peace, or literature. The awards were established by the will of Alfred Nobel, who left a fund to provide annual prizes in the five areas listed above. Ilya Prigogine Ilya Prigogine (Russian: Илья́ Рома́нович Приго́жин  and his colleagues through thermodynamics studies. Essentially the idea is that open systems decrease their entropy (order comes out of disorder) when an external energy is applied on the system. Matter organises itself under this external pressure to reach a new state where entropy has decreased. Compared to the stigmergy concept identified by Grasse, there is a fundamental difference here. Indeed, in the first case self-organisation results from a behaviour occurring from inside the system (from the ants or termites themselves). In the second case, self-organisation is the result of a pressure applied from the outside on the system.
In the 70es, through biological studies, Francisco Varela Francisco Javier Varela García (Santiago, September 7, 1946 – May 28, 2001, Paris) was a Chilean biologist, philosopher and neuroscientist who, together with his teacher Humberto Maturana, is best known for introducing the concept of autopoiesis to biology.  established the notion of autopoiesis (meaning self-production) as being the process through which an organisation is able to produce itself. Antopoiesis applies to closed systems made of autonomous components whose interactions self-maintain the system through the generation of system's components, such as living systems (cells, or organisms).
Koestler  in the late 60es established the definition of holons and holarchies. Holons are at the same time whole systems and parts of larger systems. Holarchies are hierarchies of such holons. Koestler gives a hierarchical view of self-organisation, which applies to the universe or to enterprise organisations. The idea here is that, for complex organisations, order appears from disorder, due to simple relations that statistically evolve through complex relations progressively organising themselves.
During the last 20 years, research in artificial systems has been oriented towards introducing self-organisation mechanisms specifically for software applications. These different works take diverse inspiration: from stigmergy, to autopoiesis, or to the holon concept. Recently, in addition to reproducing natural system behaviour into artificial systems, recent research efforts have been oriented towards introducing self-organisation mechanisms specifically for software applications . Section 4 describes such mechanisms in more details.
Natural self-organising 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. Other collective behaviours of animals referred to as self-organising 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.
Social behaviour of humans is also self-organised and gives rise to emergent complex global behaviours. Human beings typically work with local information and through local direct or indirect interactions producing complex societies.
Biology provides a great source of self-organising systems as well. Examples include the immune system immune system
Cells, cell products, organs, and structures of the body involved in the detection and destruction of foreign invaders, such as bacteria, viruses, and cancer cells. Immunity is based on the system's ability to launch a defense against such invaders. of mammalians, the regeneration of cells and brain behaviour.
Among artificial multi-agent based self-organising systems, we observe different trends ranging from application of naturally-inspired self-organising models, to the establishment of new mechanisms and whole infrastructures supporting self-organisation of artificial systems. Swarms provide a great source of inspiration, especially for fixed and mobile networks systems management , such as routing, load balancing , or security  Holarchies as well have inspired researchers dealing with e-Government and e-Society issues . At the level of whole infrastructures (middleware) supporting artificial self-organising systems, some works take their inspiration from magnetic fields magnetic fields,
n.pl the spaces in which magnetic forces are detectable; created by magnetostrictive ultrasonic scalers to cause the tips of instruments such as ultrasonic scalers to vibrate. , or ants .
Self-organisation essentially refers to a spontaneous, dynamically produced (re-)organisation. We present here several definitions corresponding to the different self-organisation behaviours identified in Section 2.1.
Swarm Intelligence Swarm intelligence (SI) is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. The expression "swarm intelligence" was introduced by Gerardo Beni, Susan Hackwood, and Jing Wang in 1989, in the context of . According to according to
1. As stated or indicated by; on the authority of: according to historians.
2. In keeping with: according to instructions.
3. [Bonabeau, 1999] mechanisms identifying swarms behaviour are: 1. Multiple interactions among the individuals; 2. Retroactive positive feedback (increase of pheromone when food is detected); 3. Retroactive negative feedback (pheromone evaporation); 4. Increase of behaviour modification (increase of pheromone when new path is found).
Decrease of entropy. Prigogine and his colleagues have identified four necessary requirements for systems exhibiting a self-organising behaviour under external pressure . "Mutual Causality: At least two components of the system have a circular relationship, each influencing the other. Autocatalysis autocatalysis /au·to·ca·tal·y·sis/ (-kah-tal´i-sis) catalysis in which a product of the reaction hastens the catalysis.
n. : At least one of the components is causally influenced by another component, resulting in its own increase. Far-from equilibrium condition: the system imports a large amount of energy from outside the system, uses the energy to help renew its own structures (autopoiesis), and dissipates rather than accumulates, the accruing disorder (entropy) back into the environment. Morphogenetic morphogenetic /mor·pho·ge·net·ic/ (mor?fo-je-net´ik) producing growth; producing form or shape. changes: At least one of the components of the system must be open to external random variations from outside the system. A system exhibits morphogenetic change when the components of the system are themselves changed ."
Autopoiesis. "An autopoietic system is organised (defined as a unity) as a network of processes of production (transformation and destruction) of components that produces the components that: 1. Through their interactions and transformations continuously regenerate and realise the network of processes (relations) that produced them; and 2. Constitute it (the machine) as a concrete unity in the space which they [the components] exist by specifying the topological domain of its realisation as such a network" .
Artificial Systems. Works of the Agentlink Technical Forum on Self-Organisation in MAS  have established two definitions of self-organising systems: 1. "Strong self-organising systems are systems that change their organisation without any explicit, internal or external, central control"; 2. "Weak self-organising systems are systems where reorganisation occurs as a result of internal central control or planning".
Furthermore, self-organisation implies organisation, which in turn implies some ordered structure and component behaviour. In this respect, the process of self-organisation changes the respective structure and behaviour and a new distinct organisation is self-produced.
When self-organisation meets emergence. Emergence is the fact that a structure, not explicitly represented at a lower level, appears at a higher level. In the case of dynamic self-organising systems, with decentralised control and local interactions, intimately linked with self-organisation is the notion of emergent properties. The ants actually establish the shortest path between the nest and the source of food. However in the general case, as pointed out by  self-organisation can be witnessed without emergence and vice-versa.
3 The Emergence Concept
The emergent phenomena are studied since the Greek antiquity and can be found in the writings of the Socrate periods with the notion of "the whole before the parts" or "the whole is more than all the parts". There were two different schools for studying the emergence: the proto-emergentism during the XIX century and the neo-emergentism during the XX century.
The proto-emergentists consider the emergent process as a black box (see Figure 1). Only the inputs and the outputs at the lowest level can be discerned. We don't know how the entries are transformed in outputs. Researchers such as: G.H. Lewes, C.L. Morgan, J.S. Mill, S. Alexander, D. Broad, W. Wheeler, and A.N. Whitehead try to explicit the characteristics of emergent phenomena.
[FIGURE 1 OMITTED]
From 1930 until just now, a different perspective has been envisaged, by a movement called the neo-emergentism. It has its root in dynamic of systems in Physics, in Mathematics and in Computer Science with main examples being the work of Haken, Holland, Kauffman, Langton, Prigogine, and Thom. Its aim is to develop tools, methods and constructions which enable the expression of the emergent process as less dense and by consequence as less miraculous (Figure 2). This movement tries to understand and to reproduce the process which leads to emergence.
[FIGURE 2 OMITTED]
To illustrate the notion of emergent phenomena, this section presents examples of systems where emergent phenomena can be observed.
The first example is taken from natural systems and concerns foraging ants . A foraging ant (Zool.) one of several species of ants of the genus Eciton, very abundant in tropical America, remarkable for marching in vast armies in search of food.
See also: Forage has the role to explore an environment to find food. When it finds food it comes back to the nest in tracing the path in the environment with pheromone. The shortest path to find food is the structure which emerges from the collective activity of the ants. This path has reality only for an observer of the system and an ant does not view it.
Another example concerns an application where robots have to transport boxes from always the same departure room to a destination one. Two corridors are available to go from one room to the other and two robots cannot cross in a corridor and there is no sense associated to them. The robots have a local perception. In using cooperative attitude to embody robots, we can observe corridors dedication and traffic way .
The apparition apparition, spiritualistic manifestation of a person or object in which a form not actually present is seen with such intensity that belief in its reality is created. of conscience is an example of emergent phenomenon for humans. The conscience is viewed by Searle  as a property of the brain at the higher or global level. Biologically, the brain is a complex system composed of a set of neurons and interactions between them. These neurons are the lower or micro level. Nowadays, we cannot understand or explain the conscience in observing the neurons and their interactions.
The emergence is a captivating cap·ti·vate
tr.v. cap·ti·vat·ed, cap·ti·vat·ing, cap·ti·vates
1. To attract and hold by charm, beauty, or excellence. See Synonyms at charm.
2. Archaic To capture. concept and we try to explain it in answering the following questions:
* What does emerge?
* What are the characteristics of an emergent phenomenon? These characteristics must enable to answer yes or no to the question: "is this phenomenon an emergent one?"
* What are the properties of a system producing emergent phenomena? These properties can guide designers to build systems which provide emergent phenomenon.
* What can be emergence in artificial systems? How can you decide if a program provides an emergent result or not?
The object of emergence is often called phenomenon and it can be a structure or a framework such as the Benard's cells, a behaviour such as the glider in the game of life , or a function (not as mathematic function but as the functionality of a system) such as the building of a course schedule by several local entities , .
An emergent phenomenon requires at least two levels (a micro and a macro level), and needs to be observable at least at the macro level. Its main property is the irreducibility ir·re·duc·i·ble
Impossible to reduce to a desired, simpler, or smaller form or amount: irreducible burdens.
ir of the properties of a high level theory to properties of a lower level theory . In general, there are interdependencies between the levels, the macro level constrains the micro level and the micro level causes the macro level. The phenomenon must show novelty: something new is produced that did not exist previously; must be ostensible Apparent; visible; exhibited.
Ostensible authority is power that a principal, either by design or through the absence of ordinary care, permits others to believe his or her agent possesses. ; and must produce some coherence in the sense that it has its own identity but it is strongly linked to parts that produce it  . A chain of linear activities enables explanation and predictability of a collective phenomenon. On the opposite, an emergent one needs non linear activities at the micro-level. For a given phenomenon, if most of the previous properties can be observed then the phenomenon can be qualified as emergent.
Engineers should be provided with a guide including models, tools and methods to design systems having an emergent behaviour or presenting emergent results. Furthermore, the guide should list the main properties such systems should have. To provide emergent phenomena, a system or a mechanism must at least have two levels. The system must present a dynamic during its time life. Because an emergent phenomenon is observable during time, it needs a form of self-maintained equilibrium. Nevertheless it is not a homeostatic homeostatic
pertaining to homeostasis. but dynamic equilibrium dy·nam·ic equilibrium
See equilibrium. . Emergence occurs in a narrow possibility space lying between conditions that are too ordered and too disordered. This boundary or margin is the edge of chaos
The phrase edge of chaos was coined by computer scientist Christopher Langton in 1990. The phrase originally refers to an area in the range of a variable, λ (lambda), which was varied while examining the  , which is always far from equilibrium. Near these equilibriums, a system has the ability to self-organise allowing an emergent phenomenon.
The emergence in artificial system is conceptually close to emergent computation defined by Stephanie Forrest  as follows:
* a collection of interactive agents the process;
* an epiphenomenon epiphenomenon /epi·phe·nom·e·non/ (ep?i-fe-nom´e-non) an accessory, exceptional, or accidental occurrence in the course of any disease.
n. produced by this process at the macro level;
* a natural interpretation of this epiphenomenon as computation or computation results.
An operational definition is given by the SMAC SMAC Sid Meier's Alpha Centauri (game)
SMAC Sorbitol MacConkey Agar (clinical microbiology laboratories)
SMAC Second Mitochondria-Derived Activator of Caspases (apoptosis; hematology) team at IRIT . This "technical" definition of emergence has strong computer science coloration col·or·a·tion
1. Arrangement of colors.
2. The sum of the beliefs or principles of a person, group, or institution. and it is based on two points:
1. The subject. The goal of a computational system is to realise an adequate function, judged by a relevant user. It is this function, which may evolve during time, that has to emerge.
2. The condition. This function is emergent if the coding of the system does not depend in any way of the knowledge of this function. This coding has to contain the mechanisms allowing the adaptation of the system during its coupling with the environment, so as to tend anytime towards the adequate function.
Therefore, when we design an agent for a multiagent system, the code of the agent doesn't contain any knowledge of the collective function we want the MAS to compute. As a result, no agent controls the global system.
4 Implementation in MAS
Studies on self-organisation and emergence focus on naturally inspired approaches (bio-inspired approaches , and socially-based approaches ) and non naturally inspired approaches. Researchers have been experimented with several mechanisms leading to self-organisation and often at the same time to emergent phenomenon on different kinds of applications . The different approaches can be divided in five classes depending on the mechanisms they are based on:
* direct interactions between agents using basic principles such as broadcast and localisation (programming) localisation - (l10n) Adapting a product to meet the language, cultural and other requirements of a specific target market "locale".
Localisation includes the translation of the user interface, on-line help and documentation, and ensuring the images and ;
* indirect interactions between agents and stigmergy;
* reinforcement of agent behaviours;
* cooperation behaviour of individual agents;
* choice of a generic architecture.
For a more general survey of languages and platforms for MAS implementations not directly related to self-organising mechanisms, the interested reader can refer to .
4.1 Mechanisms based on direct interactions
Zambonelli et al.  discuss different ways to engineer self-organisation. The approaches proposed consist in using few basic principles, such as localisation and broadcast, coupled with local interactions and local computations done by agents in order to provide a final coherent global state. These algorithms differ from traditional distributed algorithms A distributed algorithm is an algorithm that tries to solve a typical problem in distributed computing.
Here is a list of distributed algorithms by problem: Leader Election
Consensus in that they focus on ensuring that they eventually will converge to and maintain a desired stable state despite micro-level contingencies and any perturbations in the environment, for example changes in the network structure.
Typical examples of such mechanisms are those applied in the areas of self-assembly and distributed self-localisation where the formation of regular spatial patterns in mobile objects is required. An example is described in  where simple leader election algorithm determines the centre of gravity centre of gravity
the point in an object around which its mass is evenly distributed
Noun 1. centre of gravity of the objects and propagates it to all objects which move until a specific distance from the centre is reached. The result eventually is a circular organisation of objects. The same mechanism is used in the system for modelling fluid dynamics fluid dynamics
n. (used with a sing. verb)
The branch of applied science that is concerned with the movement of gases and liquids. . Local interactions between drops and interactions with a physical environment enable the formation of rivers or ponds.
These mechanisms focus on changing the structural aspects of the agent organisation, such as topological placement of agents and agent communication lines.
4.2 Mechanisms based on stigmergy
The self-organisation mechanisms based on the stigmergy concept aim at achieving complex system behaviours resulting of indirect interactions between agents. These interactions are due to changes in the environment. This behaviour leads towards the desired global system behaviour.
Recently, several approaches to self-organisation relying on this idea of stigmergy have been proposed and their effectiveness in achieving difficult global coordination tasks has been demonstrated. For instance, this mechanism has been used for manufacturing control , supply network management , managing computer networks security  and coordination of unmanned vehicles  Stigmergy has also been implemented with social spiders to detect regions in a scene . This principle is also used to obtain the formation of non-symmetric patterns in self-assembly applications  which in some cases are not exactly known in advance but emerge during system execution . An example of such non-symmetrical pattern formation using principles of biological formation of morphogenesis morphogenesis /mor·pho·gen·e·sis/ (mor?fo-jen´e-sis) the evolution and development of form, as the development of the shape of a particular organ or part of the body, or the development undergone by individuals who attain the type to is given in .
These mechanisms can be evaluated by experimentation Adv. 1. by experimentation - in an experimental fashion; "this can be experimentally determined"
experimentally, through an experiment , for example by simulation and prototyping , . In particular there is a tendency to integrate simulation experiments in the methodologies for engineering such systems, such as the one described in . In such approaches, the design phase involves selecting an appropriate self-organising model and verifying its correctness via experimentation. Such a model may be relevant, but not necessarily the most suitable for the particular application scenario. Therefore, the model is calibrated via iterative refinement based on the experimentation results.
In these cases, due to the non-linearity and the complexity of the phenomena involved, neither it is possible to have direct control of the system behaviour nor can it be proven that the desired behaviour will be achieved. Furthermore, the resulting system state cannot be accurately known in advance and multiple solutions can be reached. One can only obtain some statistical confidence about the system convergence to the desired globally coordinated behaviour with experimentation.
4.3 Mechanisms based on reinforcement
In some approaches self-organisation is based on the capabilities of the agents to modify dynamically their behaviour according to some reinforcement. It consists in the following basic principles: rewards increase agent behaviour and punishments decrease agent behaviour. The consequence is that an individual agent can adapt its capabilities and we can observe specialisation of roles for example. In these approaches self-organisation is based on adaptive behaviour capabilities of individual agents which are dependent on particular agent architectures. In these approaches, agents dynamically select a new behaviour (or action) based on the calculation of a probability value which is dependent on the current agent state and the perceived state of the environment, as well as on the quality of the previous adaptation decisions, for example the ones discussed in  and . Other early approaches to self-organisation that re-assign roles and responsibilities to different organisational nodes are detailed in .
A typical example of this approach is the model of adaptive agents described in . The model focuses on dynamically adapting logical relations between different behaviours, represented by roles, an agent can successively follow starting from its current state. These relations are used to select the new agent behaviour when adaptation of behaviour needs to be made. Agent behaviour is described as a graph termed behaviour graph. A behaviour graph includes two types of nodes corresponding to roles and links. Role nodes are connected to each other only via appropriate link nodes, which contain conditions specifying when the agent can switch between the respective roles. Adaptive role selection takes place on runtime based on factors associated with the links of the behaviour graph. Factors are parameters representing properties of agents and their perceived environment whose values can change dynamically during agent execution.
4.4 Mechanisms based on cooperation
The Organisation Self-Design (OSD (1) (On-Screen Display) An on-screen control panel for adjusting monitors and TVs. The OSD is used for contrast, brightness, horizontal and vertical positioning and other monitor adjustments. ) framework  uses the primitives of agents composition and decomposition. Decomposition involves division of an agent into two and can be performed to respond to overwhelming environmental demands. Composition merges two agents into one and can be useful when communication overheads between the two agents are too high. The system tries to be cooperative with its environment in creating one agent or in merging two agents in order to improve the response time to the environment. The initial organisation starts with one agent containing all domain and organisational knowledge. Simulation results demonstrate the effectiveness of the approach in adapting to changing environmental demands.
Cooperation is also used in the AMAS AMAS Address Matching Approval System (Australia Post)
AMAS Academy of Machinima Arts and Sciences
AMAS Anti Malignin Antibody in Serum (cancer test) theory  where the desired collective behaviour emerges, and can always occur as the result of cooperation , . This emergent outcome corresponds to the delivered system functionality (referred to as the global function), which is only modelled using emergence; in other words Adv. 1. in other words - otherwise stated; "in other words, we are broke"
put differently there is no agent having a global view of the system status or purpose and no centralised control. Each agent possesses the ability of self-organisation, for example the capability to locally rearrange its interactions with other agents and the environment depending on its knowledge, on its representation of the others and on the individual task it has to solve. This enables realising dynamic changes in the global system function without explicitly coding the modifications at the upper level of the system. Self-organisation is founded on the capability agents possess to be locally "cooperative". Cooperation capabilities do not imply that agents are always helpful or altruistic but they are able to recognise cooperation failures called Non Cooperative Situations (NCS (Network Call Signaling) CableLabs version of MGCP. See MGCP/MEGACO.
NCS - Network Computing System: Apollo's RPC system used by DEC and Hewlett-Packard.The protocol has been adopted by OSF. ) (which correspond to exceptions found in classical programs) and handle them. The local handling of NCS maximises the flexibility and adaptation capability of the system to unexpected situation occurring due to the dynamism of the agent interactions and the environment.
4.5 Mechanisms based on generic architecture
A particular class of self-organisation mechanisms is based on generic reference architectures or meta-models of the agents' organisation which are instantiated and subsequently dynamically modified as needed as needed prn. See prn order. according to the requirements of the particular application.
Examples of reference architectures are the mediator architecture proposed by Maturana and Norrie  and the PROSA PROSA Processing Algorithms
PROSA Product Resource Order Staff Approach  architecture which are both based on the holonic hierarchy model. The holonic hierarchy model involves structural patterns that form nested hierarchies of self-replicating structures named holarchies . The elements of holonic systems are referred to with the term holon which is a combination of the Greek word holos, meaning "whole", with the suffix "on" meaning part as in proton or neuron.
A common aspect in reference architectures is that they involve characteristic agent types from which the basic agents of a holonic organisation are derived. For example, the mediator reference architecture is based on the mediator agent type. In PROSA  the holonic organisation consists out of three types of basic holons--order holons, product holons, and resource holons. When agents are organised according to the holonic metaphor they participate in holons forming holonic structures. Self-organisation then refers to altering the holonic hierarchy following perturbations of the agent environment using a known decision making technique such as fuzzy-evolutionary reasoning .
Examples of approaches based on meta-models and architectural reflection are presented in  and . In such approaches, the current system architecture organisation is described as a particular configuration of a generic architectural meta-model which provides the architectural components and their features and also an associated set of architectural constraints that define how and when to safely reconfigure the software architecture.
The meta-model configuration can be inspected and modified at run-time. Modifications of the architecture meta-model result in modifications of the software architecture itself, and the architecture is therefore reflective. Such dynamic modifications can take place either automatically, as is the case in  or after user intervention as is done in . The common technique for representing such architectural meta-models is as a typed, directed configuration graph.
5 Strengths and Limits
Mechanisms based on direct interactions have the significant advantage that they enable the design of specific robust self-organised behaviours with exactly known outcomes. However, as mentioned in  these approaches are needed only to a limited number of applications. The reason is that only simple global equilibrium states (or patterns of activity) that can be modelled in simple linear terms can be achieved. As a result when more complex behaviour involving non-linear interactions is needed then either too many restrictions for the system operation need to be made or direct mechanisms cannot be applied.
The mechanisms based on stigmergy have additional advantages. Firstly, they enable increased reusability since they make possible to reuse the strengths of known self-organisation mechanisms from biology to build self-organising software. Secondly, once modelling and experimentation for the purposes of calibration has been carried out, the simulation models can be the basis for the actual implementation, reducing thus development time and resources required and hence facilitating development. Furthermore, the simple local behaviours they are based on are quite easy to implement, resulting in increased ease of programming. Furthermore, the multi-solution capability of these mechanisms is one of their strengths since it increases their robustness. Furthermore, although suboptimal Suboptimal
A solution is called suboptimal if a part of the solution has been optimized without regards to the overall objective. solutions are more likely to occur, the effectiveness of these mechanisms is relatively high compared to their low development cost.
The mechanisms based on cooperation behaviour, enable to treat applications with continuous or discontinuous discontinuous /dis·con·tin·u·ous/ (dis?kon-tin´u-us)
1. interrupted; intermittent; marked by breaks.
2. discrete; separate.
3. lacking logical order or coherence. global behaviour. The bottom-up design simplifies also the development and the resulting systems are robust, because adaptive. For instance, the AMAS theory guarantees that the system only adapts its behaviour to be cooperative with its environment and to satisfy it. The difficulty lies in the exhaustive list of all the non cooperative situations an agent can be faced on. Nevertheless, this is always theoretically feasible because the number of non cooperative situations related to the agent skills is enumerable e·nu·mer·a·ble
Capable of being put into one-to-one correspondence with the positive integers; denumerable.
e·numer·a·bly adv. .
However there are also disadvantages which are essentially related with harnessing emergent behaviour. Firstly, it is currently not possible to effectively control the behaviour of such systems. As a result it is common for undesired emergence states to occur . Furthermore, there can be cases where specific global states are required to emerge, such as the positions of robot players in a football game and hence the many possible solutions offered by such mechanisms can be a problem. A relevant case is when a global solution has emerged and then it is only desirable to maintain it via self-organisation and not converging to another one.
The rest of the mechanisms have similar limitations. An additional strength of mechanisms based on adaptive architectures and meta-mechanisms is that modelling is done using agent-oriented software engineering terms which increases ease of understanding by software designers--in contrast to applying a model from another discipline which would require them to obtain the necessary knowledge to master the terminology and the concepts involved. However this comes to the expense of increased difficulty in modelling global emergent behaviour.
6 Problems and Challenges
From a multi-agent systems development point of view, the central question is: how to program single agents so that, when taken as a whole, they self-organise. In the particular case of multi-agent systems, the interest and the difficulty lies in having both self-organisation and emergent properties, mainly emergent functionality that arises from individual simple tasks performed by the agents. Therefore, the engineering of self-organising applications needs means to define a global goal, and to design local behaviours so that the global behaviour emerges. This is difficult, because the global goal is not predictable as the sum or a function of the local goals. Consequently, the verification task turns out to be an arduous exercise, if not realised through simulation.
Traditional software engineering techniques are insufficient, since they are based on interfaces fixed at design time, or well established ontology ontology: see metaphysics.
Theory of being as such. It was originally called “first philosophy” by Aristotle. In the 18th century Christian Wolff contrasted ontology, or general metaphysics, with special metaphysical theories . As for current methodologies, they only make it possible to define a global behaviour when it is a function of the behaviour of the various parts.
Traditional practices in multi-agent systems introduce basic techniques for autonomously interacting or retrieving information, such as agent coordination, service description, or ontology . However, these techniques rely on pre-programmed interaction patterns, preventing adaptation to unexpected environmental changes. Current engineering practices, which directly address self-organisation, consist in designing distributed algorithms taking inspiration from natural mechanisms, both bit-inspired and socially-inspired. Some agent-oriented methodologies such as ADELFE  provide to designer means to design self-organising systems. More recently, specific electronic interaction mechanisms, non-naturally inspired, are being defined, and middleware technology developed, that will help the development of self-organising applications. However, verification and whole engineering methods remain open issues.
Currently, it is necessary to find means to "control" emergence to use it to solve problems. It is antinomic to speak about emergence and about control on the emergence. But, when designing artificial systems, it is necessary to have operational definition and tools to enable such systems to produce the wanted emergent phenomenon.
In addition the environment plays an important role both as a coordination media and as source of changes and adaptation for the agents. The environment, its engineering and its role in self-organising systems must be well understood and not be underestimated. For a deeper discussion on environments, the interested reader may refer to .
A research axis will be to find new principles, theories, models, mechanisms and methodologies to engineer self-organising systems with or without emergent phenomena. In this perspective it is important to be aware of the differences, and to distinguish solutions that tackle self-organisation issues only (without intended causal emergence); emergent issues only (without self-organisation), and solutions that intend to consider both cases in the resulting system. However, in any cases, this is a delicate problem, in the sense that unintended emergent phenomenon that have a causal effect on the system may always arise.
The growing complexity of applications needs solutions that favour autonomous, robust and adaptive systems. Natural systems must be an inspiration sources but we have to devise really new techniques, mechanisms to design self-organisation and emergent phenomenon. This new wave of systems can be called neo-computation and will be useful for designing applications in the domains such as autonomic computing Refers to computer systems and networks that configure themselves to changing conditions and are self healing in the event of failure. "Autonomic" means "automatic responses" to unpredictable events. , pervasive and ubiquitous computing.
Self-organisation and emergence interest more and more the community of computer scientists and in particular the MAS developers. This craze is due to the fact that self-organisation enables to tackle a new field of applications and that multi-agent systems are well adapted to implement self-organisation.
The paper aims are twofold: it clarifies these two concepts and proposes operational definitions; it then gives an overview of researches on self-organising MAS and emergent phenomena produced by MAS. The different mechanisms studied can be grouped into five families: direct mechanisms, characterized by simple principle of functioning in the agents and direct communication; mechanisms based on stigmergy, which use indirect interactions between agents and where the perceptions reinforce some agent actions; reinforcement mechanisms, which enable designing adaptive agents that change their roles or their behaviour in runtime; cooperative attitude of agents; and predefined architecture of the system. The paper ends in proposing some research axis such as finding new mechanisms, developing methods to design self-organising systems, providing means to control the global behaviour of the system, or proving convergence.
This work is partly supported by the Swiss NSF NSF - National Science Foundation grant 200020-105476/1 and Agentlink III.
Received: June 5, 2005
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Giovanna Di Marzo Serugendo
University of Geneva The University of Geneva (Université de Genève) is a university in Geneva, Switzerland. It was founded by John Calvin in 1559. Initially a theological seminary, it also taught law. , Switzerland
IRIT, Universite Paul Sabatier, France
University of Thessaly The University of Thessaly (Greek: Πανεπιστήμιο Θεσσαλίας) was founded in 1984. , Greece