Classical planning representation languages based on first-order logic have preliminarily been used to model and solve robotic task planning problems. Wider adoption of these representation languages, however, is hindered by the limitations present when managing implicit world changes with concise action models. To address this problem, we propose an alternative approach to representing and managing updates to world states during planning. Based on the category-theoretic concepts of $\mathsf{C}$-sets and double-pushout rewriting (DPO), our proposed representation can effectively handle structured knowledge about world states that support domain abstractions at all levels. It formalizes the semantics of predicates according to a user-provided ontology and preserves the semantics when transitioning between world states. This method provides a formal semantics for using knowledge graphs and relational databases to model world states and updates in planning. In this paper, we conceptually compare our category-theoretic representation with the classical planning representation. We show that our proposed representation has advantages over the classical representation in terms of handling implicit preconditions and effects, and provides a more structured framework in which to model and solve planning problems.

翻译：经典一阶逻辑的规划表示语言已被初步应用于机器人任务规划问题的建模与求解。然而，这些表示语言在处理简洁动作模型下的隐式世界变化时存在局限性，从而阻碍了其更广泛的应用。为解决此问题，我们提出一种替代方法，用于在规划过程中表示和管理世界状态的更新。基于范畴论中的$\mathsf{C}$-集与双推重写（DPO）概念，所提出的表示方法能够有效处理支持各层级领域抽象的世界状态结构化知识。该方法根据用户提供的本体形式化谓词语义，并在世界状态转换过程中保持语义不变。它为使用知识图谱和关系数据库建模规划中的世界状态与更新提供了形式化语义。本文从概念上将我们的范畴论表示与经典规划表示进行对比，证明所提出的表示在处理隐式前提条件与效果方面优于经典表示，并为规划问题的建模与求解提供了更具结构化的框架。