NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions
This addresses the tedious and error-prone process of manual PDDL modeling for classical planning, offering a fully automatic tool with improved reliability, though it is incremental as it builds on existing LLM and planning integration efforts.
The authors tackled the problem of automating PDDL task generation from minimal natural language descriptions, presenting NL2Plan, which outperformed direct LLM generation by achieving robust planning across seven domains, including five novel ones not in training data.
Classical planners are powerful systems, but modeling tasks in input 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 or domain-specific adaptations. We present NL2Plan, the first fully automatic system for generating complete PDDL tasks from minimal natural language descriptions. NL2Plan uses an LLM to incrementally extract the necessary information from the short text input 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 seven planning domains, five of which are novel and thus not in the LLM training data, and find that NL2Plan outperforms directly generating the files with an LLM+validator combination. As such, NL2Plan is a powerful tool for assistive PDDL modeling and a step towards solving natural language planning task with interpretability and guarantees.