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An example power system with multiple control agents is used to demonstrate the proposed modeling and analysis.
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The multi-agent system is implemented using Java Agent DEvelopment JADEE) framework and allows users to incorporate multiple control algorithms as agents for managing different tasks.
A case study has also been carried out to demonstrate the possibility of integrating multiple control algorithms in the multi-agent system and achieving a significant improvement on the overall offline learning performance.
Biological control agents can help.
Trichogramma are important biological control agents.
You can drop multiple control points.
Multiple control points are introduced.
Could biological control agent get out of control?
In this work, we used MAS to modeling several autonomous intelligent agents: Mining Rules Agent (MR-Agent), Quality Measurement Agent (QM-Agent), Decision Support Agent (DS-Agent), Principal Agent (MCA-Principal Agent), Control agent (Control Agent) and the user interface agent.
Multiple controls interact during the formation of each type of stacking pattern, including accommodation, sediment supply, and the energy of the sediment-transport agents.
Three types of agents – distribution system agent, estimator agent, and control agent are developed within the multi-agent framework.
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