Research Progress on the Application of Multi-agent Systems

Dear Colleagues,

Multi-agent systems (MAS) revolve around the design and analysis of systems comprising multiple autonomous agents, each capable of independent decision making and action. The applications of multi-agent systems span various domains, such as robotics, economics, transportation, and social sciences. In robotics, multi-agent systems enable the achievement of collaborative tasks such as search and rescue missions, while in economics, they can model complex market interactions and resource allocation. In transportation systems, multi-agent approaches are beneficial for traffic management and optimization. The importance of multi-agent systems lies in their ability to solve complex problems that cannot be effectively addressed by single entities. They promote decentralized decision making, which can enhance efficiency, adaptability, and robustness in dynamic and uncertain environments. In the era of interconnected intelligent systems, multi-agent systems play an important role in overcoming real-world challenges and are key to the development of more intelligent and autonomous systems, which involve complex problem solving. This Special Issue comprises an in-depth exploration of recent MAS applications, including innovative approaches to learning, coordination, and cooperation among autonomous agents, as well as agent-based simulations, in various fields. Topics of interest include, but are not limited to:

• Multi-agent reinforcement learning;
• Multi-agent systems for smart cities (e.g., optimizing urban infrastructure, traffic management, energy distribution);
• Multi-agent systems in healthcare (e.g., personalized patient care, remote monitoring, resource allocation in hospitals);
• Multi-agent systems for cybersecurity (e.g., coordinating the actions of security agents);
• Multi-agent systems for social networks (e.g., simulating information diffusion and opinion formation);
• Multi-agent systems in industry;
• Autonomous vehicles (e.g., cars, drones);
• Swarm intelligence;
• Multi-agent systems for e-commerce and recommendation systems;
• Multi-agent systems for edge and fog computing and federated learning;
• Multi-agent systems for disaster management;
• Multi-agent systems for social simulations;
• Multi-agent systems for environment applications.

Prof. Dr. Florin Leon
Dr. Marius Gavrilescu
Guest Editors

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• multi-agent reinforcement learning
• cooperative multi-agent systems
• agent-based modeling and simulation
• game theory in multi-agent systems
• decentralized control
• swarm intelligence
• multi-agent communication
• consensus algorithms
• conflict resolution
• trust and reputation