标题： 语言模型可以从环境反馈中推断符号规划器的动作语义 显示英文标题

标题： Language Models can Infer Action Semantics for Symbolic Planners from Environment Feedback

摘要： 符号规划器可以在给定专家定义的、领域特定的逻辑动作语义的情况下，从初始状态到目标状态发现一系列动作。 大型语言模型（LLMs）可以直接生成这样的序列，但由于推理和状态跟踪的限制，生成的计划可能不足或无法执行。 我们提出了使用语言模型预测动作语义（PSALM），通过结合符号规划器和LLMs的优势自动学习动作语义。 PSALM反复提出并执行计划，利用LLM部分生成计划，并根据执行结果推断领域特定的动作语义。 PSALM维护一个可能动作语义的信念，并在达到目标状态之前不断更新。 在7个环境中的实验表明，当仅从一个目标学习时，PSALM将计划成功率从36.4%（在Claude-3.5上）提高到100%，并且比以往的工作更高效地探索环境以推断真实的领域动作语义。 显示英文摘要

摘要： Symbolic planners can discover a sequence of actions from initial to goal states given expert-defined, domain-specific logical action semantics. Large Language Models (LLMs) can directly generate such sequences, but limitations in reasoning and state-tracking often result in plans that are insufficient or unexecutable. We propose Predicting Semantics of Actions with Language Models (PSALM), which automatically learns action semantics by leveraging the strengths of both symbolic planners and LLMs. PSALM repeatedly proposes and executes plans, using the LLM to partially generate plans and to infer domain-specific action semantics based on execution outcomes. PSALM maintains a belief over possible action semantics that is iteratively updated until a goal state is reached. Experiments on 7 environments show that when learning just from one goal, PSALM boosts plan success rate from 36.4% (on Claude-3.5) to 100%, and explores the environment more efficiently than prior work to infer ground truth domain action semantics.