MANUAL AND SEMI-AUTOMATED CLASSIFICATION IN A MICROSCOPIC ARTIFICIAL LIFE MODEL

摘要

We discuss algorithms and methods for classifying the clusters of model animals that emerge from simulations of collective behaviour in artificial life models. We show how important statistical properties for understanding scaling and universal growth can be measured from complex and chaotic model systems. We describe animal clustering algorithms and the difficulties involved in automatic tracking of herds that move and change shape, orientation and size in time. We present some heuristic rules for semi-automated classification over time and some preliminary results from our study of a predator-prey multi-agent model.