of a central body's set of instructions or regulation. Application Artificial intelligence and robotics 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. 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. See also Centralized system Decentralization Decentralized computing Distributed computing Swarm intelligence Examples of decentralized systems: Network: peer-to-peer technology (e.g. Decentralized network 42) Monetary: Bitcoin Animal communities: Red harvester ant References Further reading Social systems Systems theory