For applying symbolic planning, there is the necessity of providing the specification of a symbolic action model, which is usually manually specified by a domain expert. However, such an encoding may be faulty due to either human errors or lack of domain knowledge. Therefore, learning the symbolic action model in an automated way has been widely adopted as an alternative to its manual specification. In this paper, we focus on the problem of learning action models offline, from an input set of partially observable plan traces. In particular, we propose an approach to: (i) augment the observability of a given plan trace by applying predefined logical rules; (ii) learn the preconditions and effects of each action in a plan trace from partial observations before and after the action execution. We formally prove that our approach learns action models with fundamental theoretical properties, not provided by other methods. We experimentally show that our approach outperforms a state-of-the-art method on a large set of existing benchmark domains. Furthermore, we compare the effectiveness of the learned action models for solving planning problems and show that the action models learned by our approach are much more effective w.r.t. a state-of-the-art method.1

中文翻译：

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从部分跟踪中学习的提升操作模型


为了应用符号规划，有必要提供符号动作模型的规范，该模型通常由领域专家手动指定。但是，由于人为错误或缺乏域知识，此类编码可能会出错。因此，以自动化方式学习符号动作模型已被广泛采用作为其手动规范的替代方案。在本文中，我们专注于从一组部分可观察的计划跟踪的输入中离线学习动作模型的问题。特别是，我们提出了一种方法：（i） 通过应用预定义的逻辑规则来增强给定计划跟踪的可观察性;（ii） 从操作执行前后的部分观察中了解计划跟踪中每个操作的前提条件和效果。我们正式证明，我们的方法学习了具有基本理论特性的动作模型，这是其他方法所没有的。我们通过实验表明，我们的方法在大量现有基准域上优于最先进的方法。此外，我们比较了学习到的行动模型在解决规划问题方面的有效性，并表明通过我们的方法学习的行动模型比最先进的方法更有效。