Computational Science Technical Note CSTN-078

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Spatial Pattern Growth and Emergent Animat Segregation

K. A. Hawick and C. J. Scogings

Archived February 2009

Abstract

Spatial agent models can be used to explore self-organising effects such as pattern growth and segregation. We present and discuss an approximate timeline of key animat ideas and agent systems that have led to our animat simulation model for studying emergence effects in artificial life systems. We employ our predator-prey model to study these emergent behaviours in systems of up to around one million individual animat agents. We compare the patterns, structures and emergent properties of our model with the spatial patterns formed in non-intelligence based models that are governed only by statistical mechanics. We describe an emergent species separation effect found amongst the prey animats when we employ a simple genetic marker to track animats and introduce a microscopic breeding preference. We present results using quantitative metrics such as the animal spatial density and the pair-wise density-density correlation function. We discuss how these metrics can be used to categorize different self-organisational model regimes.

Keywords: animat; spatial agent; segregation; phase separation; self-organisation.

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@ARTICLE{CSTN-078,
  author = {K. A. Hawick and C. J. Scogings},
  title = {Spatial Pattern Growth and Emergent Animat Segregation},
  journal = {Web Intelligence and Agent Systems},
  year = {2010},
  volume = {8},
  pages = {165-179},
  number = {2},
  note = {ISSN 1570-1263},
  institution = {Massey University},
  timestamp = {2009.02.28}
}


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