论文标题
从部分和嘈杂的观测值中精确的HDDL域学习算法
An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations
论文作者
论文摘要
层次任务网络（{\ sf htn}）形式主义非常表现力，用于表达各种计划问题。与仅需要指定动作模型的经典{\ sf strips}形式主义相反，{\ sf htn}形式主义需要指定问题的任务及其分解为子任务，称为{\ sf htn}方法。因此，与经典计划问题相比，专家认为手工编码{\ sf htn}问题更困难，更容易出错。为了解决这个问题，我们提出了一种基于语法诱导的新方法（HIERAMLSI），以通过学习动作模型和{\ sf htn}方法获得{\ sf htn}计划域知识，并通过其前提条件获得。与其他方法不同，Hieramlsi能够以高水平或准确的态度学习嘈杂和部分输入观察的动作和方法。
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.
