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A decentralised system in systems theory is a system in which lower level components operate on local information to accomplish global goals. The global pattern of behaviour is an emergent property of dynamical mechanisms that act upon local components, such as indirect communication, rather than the result of a central ordering influence of a centralised system.
A centralised system is one in which a central controller exercises control over the lower-level components of the system directly or through the use of a power hierarchy (such as instructing a middle level component to instruct a lower level component). [1] The complex behaviour exhibited by this system is thus the result of the central controller's "control" over lower level components in the system, including the active supervision of the lower-level components.
A decentralised system, on the other hand, is one in which complex behaviour emerges through the work of lower level components operating on local information, not the instructions of any commanding influence. This form of control is known as distributed control, or control in which each component of the system is equally responsible for contributing to the global, complex behaviour by acting on local information in the appropriate manner. The lower level components are implicitly aware of these appropriate responses through mechanisms that are based on the component's interaction with the environment, including other components in that environment.
Decentralised systems are intricately linked to the idea of self-organisation—a phenomenon in which local interactions between components of a system establish order and coordination to achieve global goals without a central commanding influence. The rules specifying these interactions emerge from local information and in the case of biological (or biologically-inspired) agents, from the closely linked perception and action system of the agents. [2] These interactions continually form and depend on spatio-temporal patterns, which are created through the positive and negative feedback that the interactions provide. For example, recruitment in the foraging behaviour of ants relies on the positive feedback of the ant finding food at the end of a pheromone trail while ants' task-switching behaviour relies on the negative feedback of making antennal contact with a certain number of ants (for example, a sufficiently low encounter rate with successful foragers can cause a midden worker to switch to foraging, although other factors like food availability can affect the threshold for switching).
While decentralised systems can easily be found in nature, they are also evident in aspects of human society such as governmental and economic systems.
One of the most well known examples of a "natural" decentralized system is one used by certain insect colonies. In these insect colonies, control is distributed among the homogeneous biological agents who act upon local information and local interactions to collectively create complex, global behaviour. While individually exhibiting simple behaviours, these agents achieve global goals such as feeding the colony or raising the brood by using dynamical mechanisms like non-explicit communication and exploiting their closely coupled action and perception systems. Without any form of central control, these insect colonies achieve global goals by performing required tasks, responding to changing conditions in the colony environment in terms of task-activity, and subsequently adjusting the number of workers performing each task to ensure that all tasks are completed. [3] For example, ant colonies guide their global behaviour (in terms of foraging, patrolling, brood care, and nest maintenance) using a pulsing, shifting web of spatio-temporal patterned interactions that rely on antennal contact rate and olfactory sensing. While these interactions consist of both interactions with the environment and each other, ants do not direct the behaviour of other ants and thus never have a "central controller" dictating what is to be done to achieve global goals.
Instead, ants use a flexible task-allocation system that allows the colony to respond rapidly to changing needs for achieving these goals. This task-allocation system, similar to a division of labor is flexible in that all tasks rely on either the number of ant encounters (which take the form of antennal contact) and the sensing of chemical gradients (using olfactory sensing for pheromone trails) and can thus be applied to the entire ant population. While recent research has shown that certain tasks may have physiologically and age-based response thresholds, [4] all tasks can be completed by "any" ant in the colony.
For example, in foraging behaviour, red harvester ants (Pogonomyrmex barbatus) communicate to other ants where food is, how much food there is, and whether or not they should switch tasks to forage based on cuticular hydrocarbon scents and the rate of ant-interaction. By using the combined odors of forager cuticular hydrocarbons and of seeds [5] and interaction rate using brief antennal contact, the colony captures precise information about the current availability of food and thus whether or not they should switch to foraging behaviour "all without being directed by a central controller or even another ant". The rate at which foragers return with seeds sets the rate at which outgoing foragers leave the nest on foraging trips; faster rates of return indicate more food availability and fewer interactions indicate a greater need for foragers. A combination of these two factors, which are solely based on local information from the environment, leads to decisions about switching to the foraging task and ultimately, to achieving the global goal of feeding the colony.
In short, the use of a combination of simple cues makes it possible for red harvester ant colonies to make an accurate and rapid adjustment of foraging activity that corresponds to the current availability of food [6] while using positive feedback for regulation of the process: the faster outgoing foragers meet ants returning with seeds, the more ants go out to forage. [7] Ants then continue to use these local cues in finding food, as they use their olfactory senses to pick up pheromone trails laid by other ants and follow the trail in a descending gradient to the food source. Instead of being directed by other ants or being told as to where the food is, ants rely on their closely coupled action and perception systems to collectively complete the global task. [3]
While red harvester ant colonies achieve their global goals using a decentralised system, not all insect colonies function this way. For example, the foraging behaviour of wasps is under the constant regulation and control of the queen. [8]
The ant mill is an example of when a biological decentralized system fails, when the rules governing the individual agents are not sufficient to handle certain scenarios.
A market economy is an economy in which decisions on investment and the allocation of producer goods are mainly made through markets and not by a plan of production (see planned economy). A market economy is a decentralised economic system because it does not function via a central, economic plan (which is usually headed by a governmental body) but instead, acts through the distributed, local interactions in the market (e.g. individual investments). While a "market economy" is a broad term and can differ greatly in terms of state or governmental control (and thus central control), the final "behaviour" of any market economy emerges from these local interactions and is not directly the result of a central body's set of instructions or regulation.
While classic artificial intelligence (AI) in the 1970s was focused on knowledge-based systems or planning robots, Rodney Brooks' behaviour-based robots and their success in acting in the real, unpredictably changing world has led many AI researchers to shift from a planned, centralised symbolic architecture to studying intelligence as an emergent product of simple interactions. [9] This thus reflects a general shift from applying a centralised system in robotics to applying a more decentralised system based on local interactions at various levels of abstraction.
For example, largely stemming from Newell and Simon's physical-symbol theory, researchers in the 1970s designed robots with a course of action that, when executed, would result in the achievement of some desired goal; thus the robots were seen as "intelligent" if they could follow the directions of their central controller (the program or the programmer) (for an example, see STRIPS). However, upon Rodney Brooks' introduction of subsumption architecture, which enabled robots to perform "intelligent" behaviour without using symbolic knowledge or explicit reasoning, increasingly more researchers have viewed intelligent behaviour as an emergent property that arises from an agent's interaction with the environment, including other agents in that environment.
While certain researchers have begun to design their robots with closely coupled perception and action systems and attempted to embody and situate their agents a la Brooks, other researchers have attempted to simulate multi-agent behaviour and thus further dissect the phenomena of decentralised systems in achieving global goals. For example, in 1996, Minar, Burkhard, Lang-ton and Askenazi created a multi-agent software platform for the stimulation of interacting agents and their emergent collective behaviour called "Swarm". While the basic unit in Swarm is the "swarm", a collection of agents executing a schedule of actions, agents can be composed of swarms of other agents in nested structures. As the software also provides object-oriented libraries of reusable components for building models and analyzing, displaying and controlling experiments on those models, it ultimately attempts to not only simulate multi-agent behaviour but to serve as a basis for further exploration of how collective groups of agents can achieve global goals through careful, yet implicit, coordination. [10]
Examples of decentralized systems:
Stigmergy is a mechanism of indirect coordination, through the environment, between agents or actions. The principle is that the trace left in the environment by an individual action stimulates the performance of a succeeding action by the same or different agent. Agents that respond to traces in the environment receive positive fitness benefits, reinforcing the likelihood of these behaviors becoming fixed within a population over time.
Swarm behaviour, or swarming, is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is a highly interdisciplinary topic.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
Swarm robotics is an approach to the coordination of multiple robots as a system which consist of large numbers of mostly simple physical robots. ″In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act.″ It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment. This approach emerged on the field of artificial swarm intelligence, as well as the biological studies of insects, ants and other fields in nature, where swarm behaviour occurs.
Pogonomyrmex barbatus is a species of harvester ant from the genus Pogonomyrmex. Its common names include red ant and red harvester ant. These large ants prefer arid chaparral habitats and are native to the Southwestern United States. Nests are made underground in exposed areas. Their diets consist primarily of seeds, and they consequently participate in myrmecochory, an ant-plant interaction through which the ants gain nutrients and the plants benefit through seed dispersal. Red harvester ants are often mistaken for fire ants, but are not closely related to any fire ant species, native or introduced.
Carpenter ants are large ants indigenous to many forested parts of the world.
The pharaoh ant is a small (2 mm) yellow or light brown, almost transparent ant notorious for being a major indoor nuisance pest, especially in hospitals. A cryptogenic species, it has now been introduced to virtually every area of the world, including Europe, the Americas, Australasia and Southeast Asia. It is a major pest in the United States, Australia, and Europe.
Harvester ant, also known as harvesting ant, is a common name for any of the species or genera of ants that collect seeds, or mushrooms as in the case of Euprenolepis procera, which are stored in the nest in communal chambers called granaries. They are also referred to as agricultural ants. Seed harvesting by some desert ants is an adaptation to the lack of typical ant resources such as prey or honeydew from hemipterans. Harvester ants increase seed dispersal and protection, and provide nutrients that increase seedling survival of the desert plants. In addition, ants provide soil aeration through the creation of galleries and chambers, mix deep and upper layers of soil, and incorporate organic refuse into the soil.
Oecophylla smaragdina is a species of arboreal ant found in tropical Asia and Australia. These ants form colonies with multiple nests in trees, each nest being made of leaves stitched together using the silk produced by the ant larvae: hence the name 'oecophylla' [Greek for 'leaf-house'].
Eciton burchellii is a species of New World army ant in the genus Eciton. This species performs expansive, organized swarm raids that give it the informal name, Eciton army ant. This species displays a high degree of worker polymorphism. Sterile workers are of four discrete size-castes: minors, medias, porters (sub-majors), and soldiers (majors). Soldiers have much larger heads and specialized mandibles for defense. In lieu of underground excavated nests, colonies of E. burchellii form temporary living nests known as bivouacs, which are composed of hanging live worker bodies and which can be disassembled and relocated during colony emigrations. Eciton burchellii colonies cycle between stationary phases and nomadic phases when the colony emigrates nightly. These alternating phases of emigration frequency are governed by coinciding brood developmental stages. Group foraging efforts known as "raids" are maintained by the use of pheromones, can be 200 metres (660 ft) long, and employ up to 200,000 ants. Workers are also adept at making living structures out of their own bodies to improve efficiency of moving as a group across the forest floor while foraging or emigrating. Workers can fill "potholes" in the foraging trail with their own bodies, and can also form living bridges. Numerous antbirds prey on the Eciton burchellii by using their raids as a source of food. In terms of geographical distribution, this species is found in the Amazon jungle and Central America.
Spatial organization can be observed when components of an abiotic or biological group are arranged non-randomly in space. Abiotic patterns, such as the ripple formations in sand dunes or the oscillating wave patterns of the Belousov–Zhabotinsky reaction emerge after thousands of particles interact millions of times. On the other hand, individuals in biological groups may be arranged non-randomly due to selfish behavior, dominance interactions, or cooperative behavior. W. D. Hamilton (1971) proposed that in a non-related "herd" of animals, the spatial organization is likely a result of the selfish interests of individuals trying to acquire food or avoid predation. On the other hand, spatial arrangements have also been observed among highly related members of eusocial groups, suggesting that the arrangement of individuals may provide advantages for the group.
Ants are simple animals and their behavioural repertory is limited to somewhere between ten and forty elementary behaviours. This is an attempt to explain the different patterns of self-organization in ants.
Task allocation and partitioning is the way that tasks are chosen, assigned, subdivided, and coordinated within a colony of social insects. Task allocation and partitioning gives rise to the division of labor often observed in social insect colonies, whereby individuals specialize on different tasks within the colony. Communication is closely related to the ability to allocate tasks among individuals within a group. This entry focuses exclusively on social insects. For information on human task allocation and partitioning, see division of labour, task analysis, and workflow.
Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.
Pogonomyrmex occidentalis, or the western harvester ant, is a species of ant that inhabits the deserts and arid grasslands of the American West at or below 6,300 feet (1,900 m). Like other harvester ants in the genus Pogonomyrmex, it is so called because of its habit of collecting edible seeds and other food items. The specific epithet "occidentalis", meaning "of the west", refers to the fact that it is characteristic of the interior of the Western United States; its mounds of gravel, surrounded by areas denuded of plant life, are a conspicuous feature of rangeland. When numerous, they may cause such loss of grazing plants and seeds, as to constitute both a severe ecological and economic burden. They have a painful and venomous sting.
The Hodotermitidae are a basal Old World family of termites known as the harvester termites. They are distinguished by the serrated inner edge of their mandibles, and their functional compound eyes which are present in all castes. They forage for grass at night and during daylight hours, and the pigmented workers are often observed outside the nest. Their range includes the deserts and savannas of Africa, the Middle East, and Southwest Asia. Their English name refers to their habit of collecting grass, which is not unique to the family however.
Pleometrosis is a behavior observed in social insects where colony formation is initiated by multiple queens primarily by the same species of insect. This type of behavior has been mainly studied in ants but also occurs in wasps, bees, and termites. This behavior is of significant interest to scientists particularly in ants and termites because nest formation often happens between queens that are unrelated, ruling out the argument of inclusive fitness as the driving force of pleometrosis. Whereas in other species such as wasps and bees co-founding queens are often related. The majority of species that engage in pleometrosis after the initial stages of colony formation will reduce their colonies number of queens down to one dominant queen and either kill or push out the supernumerary queens. However there are some cases where pleometrosis-formed colonies keep multiple queens for longer than the early stages of colony growth. Multiple queens can help to speed a colony through the early stages of colony growth by producing a larger worker ant population faster which helps to out-compete other colonies in colony-dense areas. However forming colonies with multiple queens can also cause intra-colony competition between the queens possibly lowering the likelihood of survival of a queen in a pleometrotic colony.
| Biological swarming |  | |
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| Animal migration | ||
| Swarm algorithms | ||
| Collective motion | ||
| Swarm robotics | ||
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