The coreworld: emergence and evolution of cooperative structures in a computational chemistry
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Artificial Life: An Overview
Using multi-agent simulation to understand trading dynamics of a derivatives market
Annals of Mathematics and Artificial Intelligence
A complex adaptive system based on squirrels behaviors for distributed resource allocation
Web Intelligence and Agent Systems
Web Intelligence and Agent Systems
Survival of the Assistive. Toward biomimetic ambient intelligence
Web Intelligence and Agent Systems
Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)
Controlling non-normative behaviors by anticipation for autonomous agents
Web Intelligence and Agent Systems
Intelligent agents for traffic simulation
Proceedings of the 2008 Spring simulation multiconference
Tuning growth stability in an animat agent model
ASM '07 The 16th IASTED International Conference on Applied Simulation and Modelling
A method for avoiding the searching bias in ACO deceptive problem solving
Web Intelligence and Agent Systems
| Hi-index | 0.00 |
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.