AI Planning

AI planning focuses on developing algorithms and systems that enable computers to autonomously create plans to achieve specified goals, encompassing tasks from simple navigation to complex multi-agent coordination in uncertain environments. Current research emphasizes integrating large language models (LLMs) with classical planning techniques like A* search and developing robust frameworks for task and motion planning (TAMP), particularly in partially observable settings using models such as Partially Observable Markov Decision Processes (POMDPs). These advancements are crucial for improving the autonomy and efficiency of robots, optimizing resource allocation in various domains (e.g., logistics, manufacturing), and enhancing the security of cloud systems by automatically detecting vulnerabilities.