Self-organisation may be defined as a spontaneous (i.e. not steered or directed by an ex- ternal system) process of organisation, i.e. of the development of an organised structure as a result of many local interactions. In other words, organisation occurs without any central organising structure or entity. Such self-organisation has been observed in systems at scales from neurons to ecosystems. The cooperative behaviour of self-organising sys- tems results from local interactions between its members and not from the existence of a central controller is referred to as emergent behaviour. Swarms are one of the many self-
organising systems that are now being studied. The behaviour of such complex systems is typically unpredictable, yet exhibits various forms of adaptation and self-organisation. The idea that an ant colony is a system that organises itself without any leader is intrigu- ing. Each individual ant, acting with limited information, contributes to the emergence of an organised whole. Another typical example is an ecosystem, consisting of organisms belonging to many different species, which compete or cooperate while interacting with their shared physical environment.
When we consider a highly organised system, we usually imagine some external or internal entity that is responsible for guiding, directing or controlling that organisation [90]. For example, most human organisations have a president, chief executive or board of directors that develops the policies and coordinates the different departments. Although the controlling entity (president) is part of the system, it is in principle possible to sep- arate it from the rest. The controller is a physically distinct subsystem, that exerts its influence over the rest of the system. In this case, we may say that control is centralised. In self-organising systems, on the other hand, “control” of the organisation is typically distributed over the whole of the system. All parts contribute evenly to the resulting arrangement. Others interesting characteristics of self-organising systems are:
• Robustness or resilience — This means that self-organising systems are relatively insensitive to perturbations or errors, and have a strong capacity to restore them- selves, unlike most human designed systems. For example, an ecosystem that has undergone severe damage, such as a fire, will in general recover relatively quickly. One reason for this fault-tolerance is the redundant, distributed organisation: the non-damaged regions can usually make up for the damaged ones. Another reason for this intrinsic robustness is that self-organisation thrives on randomness, fluctuations or “noise”; a third reason for resilience, the stabilising effect of feedback loops. • Non-linearity — Most of the systems modelled by the traditional mathematical
methods of physics are linear. This means basically that effects are proportional to their causes: if you kick a ball twice as hard, it will fly away twice as fast. In self-organising systems, on the other hand, the relation between cause and effect is much less straightforward: small causes can have large effects, and large causes
can have small effects. This non-linearity can be understood from the relation
of feedback that holds between the systems components. Each component affects the other components, but these components in turn affect the first component. Thus the cause-and-effect relation is circular : any change in the first component is fed back via its effects on the other components to the first component itself. Feedback can have two basic values: positive or negative. Feedback is said to be positive if the recurrent influence reinforces or amplifies the initial change. In other words, if a change takes place in a particular direction, the reaction being fed back takes place in that same direction. Feedback is negative if the reaction is opposite to the initial action, that is, if change is suppressed or counteracted, rather than
reinforced. Negative feedback stabilises the system, by bringing deviations back to their original state. Positive feedback, on the other hand, makes deviations grow in a runaway, explosive manner. It leads to accelerated development, resulting in a radically different configuration.
In the context of MAS, 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. 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 [50]:
• direct interactions: the approaches proposed consist in using few basic principles, such as localisation and broadcast, coupled with local interactions and local com- putations done by agents in order to provide a final coherent global state. These mechanisms focus on changing the structural aspects of the agent organisation, such as topological placement of agents and agent communication lines.
• indirect interactions and stigmergy: the mechanisms aim at achieving complex sys- tem 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. 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. The re- sulting 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 behaviour with experimentation.
• reinforcement : these approaches are based on adaptive behaviour capabilities of individual agents which are dependent on particular agent architectures. Agents dynamically select a new behaviour based on the calculation of a probability value which is dependent on the current agent state and the perceived state of the envi- ronment, as well as on the quality of the previous adaptation decisions. It consists in the following basic principles: rewards increase agent behaviour and punishments decrease agent behaviour.
• cooperation: composition merges two agents into one and can be useful when com- munication 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. Simula- tion results demonstrate the effectiveness of the approach in adapting to changing environmental demands.
• 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 according to the requirements of the particular application. A common aspect in reference architec- tures is that they involve characteristic agent types from which the basic agents of a holonic organisation are derived.
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.