Multi Agent Planning

Multi-agent planning focuses on coordinating the actions of multiple autonomous agents to achieve shared goals, often in complex and dynamic environments. Current research emphasizes efficient algorithms, such as those based on heuristic search, reinforcement learning, and the integration of large language models (LLMs) with classical planners, to address challenges like long-horizon tasks, partial observability, and agent failures. These advancements are crucial for improving the performance and robustness of multi-agent systems in diverse applications, including robotics, traffic management, and communication networks.