This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of generalized plans has been a longstanding goal in AI, it remains challenging due to fundamental gaps in methods for analyzing the scope and utility of a given generalized plan. This paper addresses these gaps by developing a new conceptual framework along with proof techniques and algorithmic processes for assessing termination and goal-reachability related properties of generalized plans. We build upon classic results from graph theory to decompose generalized plans into smaller components that are then used to derive hierarchical termination arguments. These methods can be used to determine the utility of a given generalized plan, as well as to guide the synthesis and learning processes for generalized plans. We present theoretical as well as empirical results illustrating the scope of this new approach. Our analysis shows that this approach significantly extends the class of generalized plans that can be assessed automatically, thereby reducing barriers in the synthesis and learning of reliable generalized plans.

翻译：本文提出了分析和评估广义规划的新方法，该规划能够解决广泛的相关规划问题。尽管广义规划的合成与学习一直是人工智能领域的长期目标，但由于在分析给定广义规划的作用范围与实用性方面存在根本性方法空白，这一目标仍具有挑战性。本文通过建立新的概念框架，并辅以证明技术与算法流程来评估广义规划的终止性与目标可达性相关属性，从而填补这些空白。我们基于图论的经典结论，将广义规划分解为更小的组件，进而推导出分层终止论证。这些方法既可确定给定广义规划的实用性，也可指导广义规划的合成与学习过程。我们通过理论及实证结果展示了这一新方法的适用范围。分析表明，该方法显著扩展了可自动评估的广义规划类别，从而降低了合成与学习可靠广义规划的障碍。