  There is a growing interest in applying pre-trained large language models
(LLMs) to planning problems. However, methods that use LLMs directly as
planners are currently impractical due to several factors, including limited
correctness of plans, strong reliance on feedback from interactions with
simulators or even the actual environment, and the inefficiency in utilizing
human feedback. In this work, we introduce a novel alternative paradigm that
constructs an explicit world (domain) model in planning domain definition
language (PDDL) and then uses it to plan with sound domain-independent
planners. To address the fact that LLMs may not generate a fully functional
PDDL model initially, we employ LLMs as an interface between PDDL and sources
of corrective feedback, such as PDDL validators and humans. For users who lack
a background in PDDL, we show that LLMs can translate PDDL into natural
language and effectively encode corrective feedback back to the underlying
domain model. Our framework not only enjoys the correctness guarantee offered
by the external planners but also reduces human involvement by allowing users
to correct domain models at the beginning, rather than inspecting and
correcting (through interactive prompting) every generated plan as in previous
work. On two IPC domains and a Household domain that is more complicated than
commonly used benchmarks such as ALFWorld, we demonstrate that GPT-4 can be
leveraged to produce high-quality PDDL models for over 40 actions, and the
corrected PDDL models are then used to successfully solve 48 challenging
planning tasks. Resources, including the source code, are released at:
https://guansuns.github.io/pages/llm-dm.
