Spatial pattern growth and emergent animat segregation

Abstract

Spatial agent models can be used to explore self-organizing effects such as pattern growth and segregation. An approximate time line of key animat ideas and agent systems is presented and discussed. These ideas have led to a unique animat simulation model for studying emergence effects in artificial life systems and this predator-prey model is employed to study emergent behaviours in systems of up to around one million individual animat agents. The patterns, structures and emergent properties of the model are compared with the spatial patterns formed in non-intelligence based models that are governed only by statistical mechanics. An emergent species separation effect is found amongst the prey animats when a simple genetic marker is employed to track animats and introduce a microscopic breeding preference. Results are presented using quantitative metrics such as the animal spatial density and the pair-wise density-density correlation function. Ways in which these metrics can be used to categorize different self-organizational model regimes are discussed.