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• W36272803 abstract "ABSTRACT We discuss algorithms and methods for classifyingthe clusters of model animals that emerge from simu-lations of collective behaviour in artificial life models.We show how important statistical properties for un-derstanding scaling and universal growth can be mea-sured from complexand chaotic model systems. We de-scribe animal clustering algorithms and the difficultiesinvolved in automatic tracking of herds that move andchange shape, orientation and size in time. We presentsome heuristic rules for semi-automated classificationover time and some preliminary results from our studyof a predator-prey multi-agent model. KEY WORDS classification; clustering; chaotic and complex systems;multi-agent systems. 1 Introduction The problem of identifying clusters in spatial data is awell studied one in geographical, social and physicalscience applications [3,4,6]. We have employed clusterclassification methods in analysing the model configu-rations that arise in simulations of microscopic artificiallife models. Our main model involves a range of dif-ferent species that live, move around, reproduceand diein a two-dimensional or three-dimensional world, inter-acting with one another. In this paper we focus on two-dimensional“flatland” modelsandsimplified modeldy-namics that derives from fixing the life-form speciespresent, without incorporating species evolution. Themodel is essentially a predator-prey system where indi-vidual animals behave according to coded rules and ourmain interest in it is in studying the complex structuresand patterns that emerge from the collective behavioursof animals.The typical model configuration shown in figure 1arises froma particular set of parameterchoices and ini-tial random animal placements. We are interested in thestatistical averaged behaviour measured over many dif-ferent starting configurations, but with the same modelparameters. The model can be run on a parallel com-puter and many different time sequences generated(see [9]). It is useful to make movies of these sequencesand by doing so we have already identified several in-teresting emergent behaviour patterns such as defensivespiralling and various attack formations [5]. To gaina more quantitative characterisation and hence under-standing of the emergent patterns we need to be ableto identify them and plot statistical measurements. Oneuseful measure is the simple one of cluster size or ra-dius. Others include shape-moments describing orien-tation of the emergent patterns from a herd of prey un-der attack for example. The main problem in automat-ing the error-proneprocess of measuring individual pat-terns or clusters by eye is in deciding how to track clus-ters as they evolvein time. Clusters form, move, changeshape and orientation, and may dwindle away over thecourse of a simulation run as the animals die or flee. Weaim at codifying the necessary heuristics to enable an" @default.
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• W36272803 date "2005-01-01" @default.
• W36272803 modified "2023-09-26" @default.
• W36272803 title "Manual and Semi-Automated Classification in a Microscopic Artificial Life Model." @default.
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