Autonomous agents often face the challenge of interpreting uncertain natural language instructions for planning tasks. Representing these instructions as Linear Temporal Logic (LTL) enables planners to synthesize actionable plans. We introduce CoT-TL, a data-efficient in-context learning framework for translating natural language specifications into LTL representations. CoT-TL addresses the limitations of large language models, which typically rely on extensive fine-tuning data, by extending chain-of-thought reasoning and semantic roles to align with the requirements of formal logic creation. This approach enhances the transparency and rationale behind LTL generation, fostering user trust. CoT-TL achieves state-of-the-art accuracy across three diverse datasets in low-data scenarios, outperforming existing methods without fine-tuning or intermediate translations. To improve reliability and minimize hallucinations, we incorporate model checking to validate the syntax of the generated LTL output. We further demonstrate CoT-TL's effectiveness through ablation studies and evaluations on unseen LTL structures and formulas in a new dataset. Finally, we validate CoT-TL's practicality by integrating it into a QuadCopter for multi-step drone planning based on natural language instructions.

本研究解决了自主代理在解析不确定自然语言指令以进行规划时面临的挑战。提出的CoT-TL框架通过延伸链式思维推理与语义角色，有效地将自然语言规范转换为线性时态逻辑（LTL）表示，显著提高了透明性与可信度，且在低数据环境中达到最先进的准确率。这一成果不仅改善了生成LTL的可靠性，还有助于在真实应用（如无人机多步骤规划）中验证其实用性。

CoT-TL：低资源时态知识表示的规划指令利用链式思维推理