Multi Agent Interaction

Multi-agent interaction research focuses on understanding and modeling how multiple autonomous agents interact and cooperate or compete within a shared environment, aiming to improve their collective decision-making and behavior. Current research heavily utilizes large language models (LLMs) and deep reinforcement learning (DRL) to design agents capable of complex interactions, often employing graph neural networks, attention mechanisms, and diffusion models to capture agent relationships and predict future behavior. This field is crucial for advancing autonomous systems in diverse areas like robotics, autonomous driving, and traffic management, as well as providing insights into collective intelligence and emergent behavior in complex systems.