Adaptive Multi-Agent Intelligence for Real-Time Decision Optimization in Dynamic Environments
Abstract
This study presents an adaptive multi-agent intelligence framework designed to enhance real-time decision optimization in fast-changing environments. The approach integrates cooperative agents empowered with reinforcement learning, context modeling, and predictive analytics to analyze diverse data streams and adjust strategies autonomously. A dynamic coordination mechanism enables agents to share insights, negotiate actions, and self-correct based on environmental shifts. Experiments across logistics routing, energy grid balancing, and traffic flow management demonstrate significant improvements in response time, stability, and overall system efficiency. The findings highlight the potential of multi-agent AI systems to support resilient, scalable, and high-accuracy decision-making in complex operational domains.
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