Symbolic Planning

Symbolic planning aims to automate the creation of action sequences (plans) to achieve complex goals, bridging the gap between high-level reasoning and low-level execution. Current research focuses on improving the efficiency and scalability of planners, exploring novel architectures like transformers and integrating them with large language models and affordance-based representations to enhance generalization and handle uncertainty. These advancements are significant for robotics, AI, and other fields requiring automated decision-making in complex, dynamic environments, enabling more robust and adaptable intelligent systems.