Autonomous decision systems have become essential in modern intelligent computing, driven by advances in AI and distributed computing. This paper studies multi-agent AI (MAAI) systems, focusing on their theoretical foundations, design methods, and performance before 2018. Multi-agent systems enable decentralized, scalable, and adaptive decision-making by distributing intelligence among interacting agents capable of perception, reasoning, and action. The paper highlights how agent-based models integrate with decision frameworks, where cooperation, coordination, and competition lead to intelligent behavior. It reviews approaches such as rule-based systems, utility models, and reinforcement learning in multi-agent contexts, while addressing challenges like scalability, communication overhead, conflict resolution, and uncertainty. It also examines key developments in distributed AI, including contract net protocols, distributed constraint satisfaction, and game-theoretic methods, along with applications in robotics, smart grids, traffic, and defense. Finally, it discusses system evaluation metrics like efficiency, convergence, and fault tolerance, offering a consolidated reference and identifying future research directions.
Ibrahim A. Lawal, M. S, Ansari K· International Journal of Art...· 0 citations
The Intelligent Traffic Management Systems (ITMS) have become an important feature of the smart city infrastructure because of the fast increase in the city population and the consequent urban traffic congestion, fuel use, and road accidents. Conventional methods of traffic management apply a lot on the operation of fixed-time control mechanisms and rule-based systems, which are not flexible to the dynamic traffic conditions. In the recent past, progress in the field of deep learning has resulted in the creation of data-driven systems of traffic management that can learn intricate spatial and temporal patterns of traffic through major sources of heterogeneous data. The paper provides a detailed research on designing, implementing and testing of an Intelligent Traffic Management System based on deep learning. The suggested system combines convolutional neural networks (CNNs) to estimate the traffic density, recurrent neural networks (RNNs) and long short-term memory (LSTMs) to predict the traffic flow, and reinforcement learning (RLs) to control traffic signals. Various data sources such as live video streams, sensor data and past traffic data are used to improve accuracy of predictions and effectiveness of decisions. The proposed system architecture includes a modular architecture that will include all the layers of data acquisition, preprocessing, model training, and real-time deployment. Numerous experiments on benchmark traffic datasets have shown that congestion is greatly reduced, the average vehicle waiting time is minimized and the traffic throughput is much improved in comparison to traditional systems. The findings confirm that traffic management systems based on deep learning can contribute significantly to the improvement of urban mobility, environmental impact, and road safety. This paper has given relevant information about the application of AI-driven traffic control systems in practice and opened up the prospects of future research in intelligent transportation systems.
Ibrahim A. Lawal· International Journal of Art...· 0 citations
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