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Simulating a large scale agent-based STS that is emphasized on crowd events is thus an interesting topic for researchers (Henein and White 2005).
Successive releases of Repast have extended the system to handle large-scale agent simulation application development.
To develop a model to support the analysis and modeling of complex, large-scale agent systems.
To simulate a large-scale agent based model, the most traditional way is to distribute it across a set of processors.
The goal of this research is to develop a framework to support the analysis and modeling of complex, large-scale agent systems.
Furthermore, the objectives of this paper are as follows: To develop a model to support the analysis and modeling of complex, large-scale agent systems.
FLAME (Flexible Large-scale Agent Modelling Environment) [8] (http://www.flame.ac.uk) is an agent-based modelling framework developed at the University of Sheffield and has previously been used to model the emergent behaviour of various biological systems (e.g. keratinocyte colony formation [5] and urothelial cells [10]).
In order to circumvent the dimensionality issues associated to the control of large-scale agent-based models, we follow a Boltzmann approach.
This paper describes dynamic agent composition, used to support the development of flexible and extensible large-scale agent-based models (ABMs).
Bozada et al. (2006) presented one of the first implementations the Repast watcher pattern in a large-scale, agent-based model of military deployment logistics.
While the concept of dynamic agent composition is quite simple, its application to the implementation of large-scale agent-based models with an underlying networked structure has its challenges.
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