To develop a model to support the analysis and modeling of complex, large-scale agent systems.
His research interests are in areas of robotics, simulation, modeling of complex systems and nonlinear control.
We discuss some issues involved in modeling of complex systems composed of dynamically interacting agents.
Cognitive science provides a framework for the analysis and modeling of complex human performance.
In recent years, Artificial Neural Networks (ANNs) have opened alternative pathways for modeling of complex and nonlinear processes.
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.
On the other hand (logical) modeling of complex systems is trying to cover a comprehensive, integrated and global view of the whole enterprise (enterprise modeling).
Computational intelligent techniques, such as fuzzy and genetic algorithm, have proven to be useful in modeling of complex nonlinear phenomena such as dynamic compaction.
Detailed modeling of complex combustion kinetics remains challenging and often intractable, due to prohibitive computational costs incurred when solving the associated large kinetic mechanisms.
Hierarchical structuring facilitates modeling of complex biological systems by defining them in terms of their components and the interactions that exist between them.
