Guided Self Organization in Complex Adaptive Systems
The following pertains to a new line of research on the topic of
guided self-organization that was discussed with researchers at ELSI
during my visit in the period December 5, 2017 – January 5, 2018.
This research proposes a novel computational method for enabling
Guided Self-Organisation (GSO) in artificial complex adaptive systems.
Complex adaptive systems are networks comprising many components
(nodes), where nodes are highly connected, there is no central
control, but where sophisticated global self-organising behaviour is
exhibited resultant of local interactions between nodes.
Self-organisation is a ubiquitous emergent phenomenon in many natural
complex systems. For example, collective gathering and construction
behaviour in some social insects, cortical map formation during
neuronal development in animal brains, and swarming behaviour to ward
off predators. Examples of artificial complex adaptive systems
include the Internet, major urban area traffic flow, and swarm robotic
systems. GSO is the study of how to manipulate the complex system
nodes and their interactions so as new patterns and structures emerge,
guiding the system towards a desired state.
The potential impact of this line of research is that human designers
of artificial complex adaptive systems devise computational methods
that adapt the behaviour and interactions of individual nodes such
that the complex system self-regulates and appropriate (user desired)
global behaviours emerge in response to external (the system’s
environment) and internal (system node) changes. That is, GSO
(described by new computational methods) would ensure that an
artificial complex system self-regulates given external pressures or
component failures, meaning the system reconfigures itself as required
and continues to properly function. Thus, if GSO were to be
successfully implemented as a governing computational method that
could self-organise the global behaviour of any artificial complex
system, then such artificial complex systems would be far more
resilient and robust to damage and changes in their environment. For
example, automated self-organisation of problem solving behaviour to
cope with hub failures and network traffic congestion would be highly
desirable to increase the robustness and resilience of large scale
computer networks.
Currently there is no theoretical or practical formalisation of GSO
principles, though such a formalisation (for example, a computational
method or mathematical theory) would have many applications in a
diverse range of disciplines.
The research problem is thus how the behaviours of individual system
components and their interactions (for example, computer network
nodes) must be adapted in order that the system (for example, a
large-scale computer network) produces a desired solution (for
example, maintaining optimal traffic flow given disruptions to
specific nodes and network connectivity).
Other envisaged applications include applying GSO for adapting the
collective traffic flow of autonomous vehicles that must efficiently
navigate urban road networks, self-assembly and adaptation of complex
nano-structures used in engineering new materials, and swarm robotic
behaviours that emerge to effectively and efficiently solve complex
physical tasks such as collective construction.