  Today's classical planners are powerful, but modeling input tasks in formats
such as PDDL is tedious and error-prone. In contrast, planning with Large
Language Models (LLMs) allows for almost any input text, but offers no
guarantees on plan quality or even soundness. In an attempt to merge the best
of these two approaches, some work has begun to use LLMs to automate parts of
the PDDL creation process. However, these methods still require various degrees
of expert input. We present NL2Plan, the first domain-agnostic offline
LLM-driven planning system. NL2Plan uses an LLM to incrementally extract the
necessary information from a short text prompt before creating a complete PDDL
description of both the domain and the problem, which is finally solved by a
classical planner. We evaluate NL2Plan on four planning domains and find that
it solves 10 out of 15 tasks - a clear improvement over a plain
chain-of-thought reasoning LLM approach, which only solves 2 tasks. Moreover,
in two out of the five failure cases, instead of returning an invalid plan,
NL2Plan reports that it failed to solve the task. In addition to using NL2Plan
in end-to-end mode, users can inspect and correct all of its intermediate
results, such as the PDDL representation, increasing explainability and making
it an assistive tool for PDDL creation.
