Abstract
Self-organisation is pervasive: neuronal ensembles self-organise into complex spatio-temporal spike patterns which facilitate synaptic plasticity and long-term consolidation of information; large-scale natural or social systems, as diverse as forest fires, landslides, or epidemics, produce spontaneous scale-invariant behaviour; robotic modules self-organise into coordinated motion patterns; individuals within a swarm achieve collective coherence out of isolated actions; and so on. Selforganisation is also valuable: the resultant increase in an internal organisation brings benefits to the (collective) organism, be it a learning brain, a co-evolving ecosystem, an adapting modular robot, or a re-configuring swarm. These benefits are typically realised in increased resilience to external disturbances, adaptivity to novel tasks, and scalability with respect to new challenges. However, self-organisation is difficult to engineer on demand: the intricate fabric of interactions within a self-organising system cannot follow a simple-minded blueprint and resists crude interventions.
Keywords
- Cellular Automaton
- Transfer Entropy
- Modular Robot
- Information Cascade
- Reservoir Computing
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Prokopenko, M., Polani, D., Ay, N. (2014). On the Cross-Disciplinary Nature of Guided Self-Organisation. In: Prokopenko, M. (eds) Guided Self-Organization: Inception. Emergence, Complexity and Computation, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53734-9_1
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