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By adopting the immunity mechanisms of AIS adapted to specify and implement the behavior of each agent, a behavioral control paradigm is developed.
Given the market prices, we describe the behavior of each agent.
Emergent behavior results from the individual behavior of each agent, and spatio-temporally determined interactions among agents [ 20].
We encountered some limitations, including the major difficult to have accurate financial hospital data and detailed information on the behavior of each agent within this complicated context.
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The behaviors of each agent group are identified by qualitative surveys and are considered separately using multi-criteria analysis.
In agent-based models, a simulation is run over extended periods of hypothetical time, and the behavior of each unique agent in the ecosystem (each virus, each person and its immunological history) is tracked computationally during this period and under multiple scenarios.
The behavior of each tamponade agent was quantified by measuring (1) the maximum height of the tamponade bubble, and calculating (2) the arc of retinal contact subtended by the tamponade bubble.
We developed an agent-based model to simulate a signaling cascade which allowed us to focus on the behavior of each class of agents independently of the other classes except when they were in physical contact.
It is a multi-sectoral model and explains the explicit information about the behavior of economic agent.
Swarm Intelligence (SI) is research inspired by observing the naturally intelligent behavior of biological agent swarms within their environments.
The behavior of the agent can easily be mapped on to real life robots for practical applications.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com