<![CDATA[The rapid growth of large-scale smart environments, including smart cities, autonomous transportation systems, and smart grids, has necessitated advanced decision-making mechanisms capable of adapting to dynamic and complex conditions. Intelligent agent systems (IAS) offer a promising solution by enabling decentralized, autonomous decision-making based on real-time data. This study explores the application of IAS for adaptive decision-making in large-scale smart environments, focusing on the challenges of scalability, resource allocation, and system responsiveness. The primary objective is to design and evaluate an intelligent agent system capable of operating efficiently in diverse, complex environments. A mixed-methods approach was used, combining simulations and real-world implementations in various smart environments, including energy grids, smart cities, and industrial automation systems. The results indicate that while IAS can perform effectively in smaller environments, performance decreases in large-scale systems due to increased agent interaction and data complexity. Scalability and adaptability remain significant challenges, with response times and resource allocation efficiency declining as the system size grows. The study concludes that further advancements are required in communication protocols and machine learning algorithms to enhance the scalability and real-time decision-making of IAS in large, interconnected systems.]]>
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