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A Tool for Automated Analysis of Collective Behaviour Models
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Andreagiovanni Reina
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University of Sheffield, UK |
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| On 2017-11-23 at 15:30:00 (Brussels Time) |
Abstract
Collective behaviour is of fundamental importance to both the life sciences, where it appears at all levels of biological complexity from single cells to superorganisms, and the physical and engineering sciences, where it describes physical phenomena, and can be used to design distributed systems. Yet, reasoning about collective behaviour is inherently difficult, as the nonlinear interactions between individuals give rise to complex emergent dynamics. Mathematical tools have been developed to systematically analyse collective behaviour in such systems, yet these frequently require extensive training in formal modelling and technical ability to apply. Even for engineers and physical scientists, analysis using these tools can be a laborious and time-consuming endeavour. Together, these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here, w e present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by life scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. Our tool can be accessed online without installing software, uses the simples possible programmatic interface, and provides interactive graphical plots for users to develop understanding of their models. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that cou ld take significant effort are substantially facilitated.
Keywords
Swarm robotics, Collective behaviour
