Sequential Planning

Sequential planning focuses on generating ordered sequences of actions to achieve a goal, addressing challenges in areas like robotics, multi-agent systems, and natural language processing. Current research emphasizes improving the efficiency and scalability of planning algorithms, particularly through hybrid approaches combining symbolic planning with machine learning models like large language models (LLMs) and reinforcement learning, as well as exploring novel architectures like space-time planners and iterative planning frameworks. These advancements are crucial for enabling more robust and adaptable autonomous systems across diverse applications, from autonomous navigation and resource allocation to creative text generation and human-robot interaction.