Summary:<p>The paper introduces LLM+P, a framework that combines the strengths of classical planners with large language models (LLMs) to solve long-horizon planning problems. LLM+P takes in a natural language description of a planning problem, converts it into a PDDL file, leverages classical planners to find a solution, and then translates the solution back into natural language. The authors provide a set of benchmark problems and find that LLM+P is able to provide optimal solutions for most problems, while LLMs fail to provide even feasible plans for most problems. The paper suggests that LLM+P can be used as a natural language interface for giving tasks to robot systems. The authors also propose that classical planners can be another useful external module for improving the performance of downstream tasks of LLMs. The paper highlights the importance of providing context (i.e., an example problem and its corresponding problem PDDL) for in-context learning, and suggests future research directions to further extend the LLM+P framework.<p>PPDL: <a href="https://en.wikipedia.org/wiki/Planning_Domain_Definition_Language" rel="nofollow">https://en.wikipedia.org/wiki/Planning_Domain_Definition_Lan...</a>