The Hierarchical Task Network ({\sf HTN}) formalism is very expressive and used to express a wide variety of planning problems. In contrast to the classical {\sf STRIPS} formalism in which only the action model needs to be specified, the {\sf HTN} formalism requires to specify, in addition, the tasks of the problem and their decomposition into subtasks, called {\sf HTN} methods. For this reason, hand-encoding {\sf HTN} problems is considered more difficult and more error-prone by experts than classical planning problem. To tackle this problem, we propose a new approach (HierAMLSI) based on grammar induction to acquire {\sf HTN} planning domain knowledge, by learning action models and {\sf HTN} methods with their preconditions. Unlike other approaches, HierAMLSI is able to learn both actions and methods with noisy and partial inputs observation with a high level or accuracy.

翻译：等级任务网络(lsf HTN}) 形式主义非常明确,用来表达各种各样的规划问题。 与传统传统形式主义相比,传统形式主义只需要具体指定行动模式,而传统形式主义则需要规定问题的任务及其分解为子任务,称为 tsf HTN} 方法。 因此,专家认为手工编码问题比传统规划问题更困难、更易出错。 为了解决这一问题,我们提议一种基于语法感应的新方法(HierAMLSI),通过学习行动模式和Sf HTN}方法及其先决条件来获取域知识。 与其他方法不同, HierAMLSI能够以高水平或准确度的噪音和部分投入观察方法学习行动和方法。