Decentralised system

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 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.

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.

[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.

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.

[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.

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.

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".