Multi Agent Decision

Multi-agent decision-making focuses on how multiple autonomous agents can coordinate their actions to achieve individual and collective goals, often in complex and uncertain environments. Current research emphasizes developing efficient algorithms, such as Monte Carlo Tree Search and various reinforcement learning approaches (including those leveraging large language models and graph neural networks), to enable effective collaboration and competition among agents. These advancements are crucial for improving the performance of systems ranging from autonomous vehicles navigating traffic to robots collaborating in industrial settings, and for understanding fundamental principles of social intelligence and decision-making.