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Adaptiveness of self-organised decision making

Title
Adaptiveness of self-organised decision making
Funding
ARC | Discovery Projects
Contract (GA) number
DP0879239
Start Date
2008/01/01
End Date
2011/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP0879239

 

  • Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

    Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey (2014)
    Projects: ARC | Adaptiveness of self-organised decision making (DP0879239)
    We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integr...

    Stochastic simulations of pattern formation in excitable media.

    We present a method for mesoscopic, dynamic Monte Carlo simulations of pattern formation in excitable reaction-diffusion systems. Using a two-level parallelization approach, our simulations cover the whole range of the parameter space, from the noise-dominated low-particle number regime to the quasi-deterministic high-particle number limit. Three qualitatively different case studies are performed that stand exemplary for the wide variety of excitable systems. We present mesoscopic stochastic ...

    Multi-Dimensional, Mesoscopic Monte Carlo Simulations of Inhomogeneous Reaction-Drift-Diffusion Systems on Graphics-Processing Units

    Vigelius, Matthias; Meyer, Bernd (2012)
    Projects: ARC | Adaptiveness of self-organised decision making (DP0879239)
    For many biological applications, a macroscopic (deterministic) treatment of reaction-drift-diffusion systems is insufficient. Instead, one has to properly handle the stochastic nature of the problem and generate true sample paths of the underlying probability distribution. Unfortunately, stochastic algorithms are computationally expensive and, in most cases, the large number of participating particles renders the relevant parameter regimes inaccessible. In an attempt to address this problem ...
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