Many real-world tasks involve a mixed-initiative setup, wherein humans and AI systems collaboratively perform a task. While significant work has been conducted towards enabling humans to specify, through language, exactly how an agent should complete a task (i.e., low-level specification), prior work lacks on interpreting the high-level strategic intent of the human commanders. Parsing strategic intent from language will allow autonomous systems to independently operate according to the user's plan without frequent guidance or instruction. In this paper, we build a computational interface capable of translating unstructured language strategies into actionable intent in the form of goals and constraints. Leveraging a game environment, we collect a dataset of over 1000 examples, mapping language strategies to the corresponding goals and constraints, and show that our model, trained on this dataset, significantly outperforms human interpreters in inferring strategic intent (i.e., goals and constraints) from language (p < 0.05). Furthermore, we show that our model (125M parameters) significantly outperforms ChatGPT for this task (p < 0.05) in a low-data setting.

翻译：许多现实世界任务涉及混合主动式设置，即人类与AI系统协作完成任务。尽管已有大量研究致力于让人类通过语言精确指定智能体如何完成任务（即低层级规范），但先前工作在解读人类指挥官的高层战略意图方面仍存在不足。从语言中解析战略意图将使自主系统能够根据用户计划独立运作，无需频繁指导或指令。本文构建了一个计算接口，能够将非结构化语言策略转化为以目标和约束形式呈现的可执行意图。借助游戏环境，我们收集了包含1000多个示例的数据集，将语言策略映射至相应目标和约束。实验表明，在此数据集上训练的模型在从语言中推断战略意图（即目标和约束）方面显著优于人类解读员（p < 0.05）。此外，我们证明在低数据环境下，该模型（1.25亿参数）在此任务上的表现显著优于ChatGPT（p < 0.05）。