Planning is a fundamental activity, arising frequently in many contexts, from daily tasks to industrial processes. The planning task consists of selecting a sequence of actions to achieve a specified goal from specified initial conditions. The Planning Domain Definition Language (PDDL) is the leading language used in the field of automated planning to model planning problems. Previous work has highlighted the limitations of PDDL, particularly in terms of its expressivity. Our interest lies in facilitating the handling of complex problems and enhancing the overall capability of automated planning systems. Unified-Planning is a Python library offering high-level API to specify planning problems and to invoke automated planners. In this paper, we present an extension of the UP library aimed at enhancing its expressivity for high-level problem modelling. In particular, we have added an array type, an expression to count booleans, and the allowance for integer parameters in actions. We show how these facilities enable natural high-level models of three classical planning problems.

翻译：规划是一项基础活动，频繁出现在从日常任务到工业流程的众多场景中。规划任务的核心在于从给定的初始条件出发，选择一系列动作以实现特定目标。规划领域定义语言（PDDL）是自动规划领域用于建模规划问题的主流语言。先前的研究已指出PDDL的局限性，尤其在表达能力方面。我们的研究兴趣在于促进复杂问题的处理，并提升自动规划系统的整体能力。Unified-Planning是一个Python库，它提供了用于指定规划问题并调用自动规划器的高层API。本文介绍了UP库的一个扩展，旨在增强其面向高层问题建模的表达能力。具体而言，我们增加了数组类型、布尔值计数表达式，并允许动作中使用整数参数。我们展示了这些功能如何实现对三个经典规划问题的自然高层建模。