Symbolic Planner

Symbolic planning aims to create automated systems capable of generating sequences of actions to achieve complex goals, traditionally using rule-based representations. Current research heavily focuses on integrating large language models (LLMs) with symbolic planners, leveraging LLMs' ability to handle natural language and commonsense reasoning while retaining the structured approach of symbolic methods. This neuro-symbolic approach addresses limitations of both pure symbolic and LLM-based planners, improving efficiency and success rates in various domains, including robotics and task automation. The resulting advancements hold significant potential for improving the capabilities of autonomous systems and human-robot collaboration.