AI and Machine Learning

Multi-Agent Systems

Multi-Agent Systems (MAS) consist of multiple intelligent agents interacting within shared environments, either collaboratively or competitively. These systems are applied in traffic management, distributed robotics, smart grids, resource allocation, and simulation of complex socio-technical systems. MAS rely on coordination, negotiation, and decentralized decision-making to achieve collective goals while adapting to dynamic conditions. Reinforcement learning and other AI techniques enhance both cooperative and competitive strategies among agents. Research in MAS focuses on scalability, robustness, and emergent behavior, enabling the design of intelligent, distributed ecosystems where agents autonomously optimize outcomes, improve efficiency, and tackle complex, real-world problems.