Planning Domain

Planning domain research focuses on developing algorithms and models that enable artificial agents to efficiently determine sequences of actions to achieve goals, addressing challenges like incomplete information and long horizons. Current research emphasizes integrating large language models (LLMs) with classical planning techniques, utilizing architectures like graph neural networks (GNNs) and transformers to improve heuristic function learning and plan generation, and exploring affordance-based representations for more robust scene understanding. These advancements are crucial for creating more capable autonomous agents in diverse applications, ranging from robotics and industrial automation to complex question answering and knowledge worker assistance.