Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists ofthree layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number ofagents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent’s behavior. The second layer, a global environment layer consists ofa specifically designed kinetic data structure to track efficiently the immediate environment ofeach agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem oftarget tracking is generalized in a way that allows navigation in maze-like terrains, avoidance oflocal minima and cooperation beten agents.  implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output.  also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature ofour agent steering.  demonstrate the por ofthe approach through a series ofexperiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments. r 2001 Published by Elsevier Science Ltd.
['Siome Goldenstein', 'Menelaos I. Karavelas', 'Dimitris N. Metaxas', 'Leonidas J. Guibas', 'Eric Aaron', 'Ambarish Goswami']
I am writing a research paper on a idea Scalable nonlinear dynamical systems for agent steering and crowd simulation
[{"from": "human", "value": "I am writing a research paper on a idea Scalable nonlinear dynamical systems for agent steering and crowd simulation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new methodology for agent modeling that is scalable and efficient. It is based on the integration of nonlinear dynamical systems and kinetic data structures. The method consists ofthree layers, which together model 3D agent steering, crowds and flocks among moving and static obstacles. The first layer, the local layer employs nonlinear dynamical systems theory to models low-level behaviors. It is fast and efficient, and it does not depend on the total number ofagents in the environment. This dynamical systems-based approach also allows us to establish continuous numerical parameters for modifying each agent\u2019s behavior. The second layer, a global environment layer consists ofa specifically designed kinetic data structure to track efficiently the immediate environment ofeach agent and know which obstacles/agents are near or visible to the given agent. This layer reduces the complexity in the local layer. In the third layer, a global planning layer, the problem oftarget tracking is generalized in a way that allows navigation in maze-like terrains, avoidance oflocal minima and cooperation beten agents.  implement this layer based on two approaches that are suitable for different applications: One approach is to track the closest single moving or static target; the second is to use a pre-specified vector field, which may be generated automatically (with harmonic functions, for example) or based on user input to achieve the desired output.  also discuss how hybrid systems concepts for global planning can capitalize on both our layered approach and the continuous, reactive nature ofour agent steering.  demonstrate the por ofthe approach through a series ofexperiments simulating single/multiple agents and crowds moving towards moving/static targets in complex environments. r 2001 Published by Elsevier Science Ltd."}]