The University of Southampton
University of Southampton Institutional Repository

Optimisation in ‘Self-modelling’ Complex Adaptive Systems

Watson, Richard A., Buckley, C. L. and Mills, Rob (2011) Optimisation in ‘Self-modelling’ Complex Adaptive Systems Complexity, 16, (5), pp. 17-26.

Record type: Article

Abstract

When a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems.

PDF Watson_Buckley_Mills_Complexity_in-press.pdf - Accepted Manuscript
Download (398kB)

More information

Published date: 23 May 2011
Organisations: Agents, Interactions & Complexity, EEE

Identifiers

Local EPrints ID: 271051
URI: http://eprints.soton.ac.uk/id/eprint/271051
PURE UUID: 19605443-b486-4e04-a573-46483b36bd02

Catalogue record

Date deposited: 10 May 2010 15:04
Last modified: 18 Jul 2017 06:47

Export record

Contributors

Author: C. L. Buckley
Author: Rob Mills

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×