Modelling Large Scale Autonomous Systems

Many large scale autonomous systems based on a large number of interacting agents in a structured physical environment have emerged in diverse areas such as biology, ecology or finance. Inspired by the desire to better understand and make the best out of such systems, we model them in order to gain insight, predict the future and control it partially if not fully. In this paper, we present a stochastic approach to modeling such systems based on G-networks. We propose two methods which deal with cases where complete or incomplete world knowledge is available. We use strategic military planning in urban scenarios as an example to demonstrate our approach. Our results suggest that this approach tackles the problem of modeling autonomous systems at low computational cost. Apart from offering numerical estimates of various outcomes, the approach helps us identify the parameters or characteristics that have the greatest impact on the system most and allows us to compare alternative strategies