A closed-loop state-centric multi-agent framework for robust passenger load estimation from heterogeneous data streams using physical feasibility enforcement and dynamic trust allocation.
Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems
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A Closed-loop, State-centric, Multi-agent Framework for Passenger Load Estimation from Heterogeneous Data Streams
A closed-loop state-centric multi-agent framework for robust passenger load estimation from heterogeneous data streams using physical feasibility enforcement and dynamic trust allocation.