Classical planning approaches guarantee finding a set of actions that can
achieve a given goal state when possible, but require an expert to specify
logical action semantics that govern the dynamics of the environment.
Researchers have shown that Large Language Models (LLMs) can be used to
directly infer planning steps based on commonsense knowledge and minimal domain
information alone, but such plans often fail on execution. We bring together
the strengths of classical planning and LLM commonsense inference to perform
domain induction, learning and validating action pre- and post-conditions based
on closed-loop interactions with the environment itself. We propose PSALM,
which leverages LLM inference to heuristically complete partial plans emitted
by a classical planner given partial domain knowledge, as well as to infer the
semantic rules of the domain in a logical language based on environment
feedback after execution. Our analysis on 7 environments shows that with just
one expert-curated example plans, using LLMs as heuristic planners and rule
predictors achieves lower environment execution steps and environment resets
than random exploration while simultaneously recovering the underlying ground
truth action semantics of the domain.

提出使用经典规划和大型语言模型共同实施领域归纳、学习和验证动作的前后条件，利用 LLM 推理来启发性地完成经典规划器发出的部分计划，并根据执行后的环境反馈用逻辑语言推断领域的语义规则。通过对 7 个环境的分析表明，使用 LLMs 作为启发性规划器和规则预测器仅需一个专家精心策划的示例计划，就能够比随机探索更少的执行步骤和环境重置，并同时恢复领域的基本行动语义。