Quantitative Modeling of Multi-Agent Systems
نویسنده
چکیده
This paper presents a network centric method of modeling multi-agent systems in a decentralized data fusion, feature localization scenario. The robotic multi-agent system is abstracted using a matrix of metric interactions stored within a Dynamic Bayesian Network. This model, called a system map, provides a tool which may be used in automated design of multiple robot systems. Results from Monte Carlo simulations are presented which demonstrate how system maps can be an accurate model for robotic systems.
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