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Classical planning representation languages based on first-order logic havepreliminarily been used to model and solve robotic task planning problems.Wider adoption of these representation languages, however, is hindered by thelimitations present when managing implicit world changes with concise actionmodels. To address this problem, we propose an alternative approach torepresenting and managing updates to world states during planning. Based on thecategory-theoretic concepts of $\mathsf{C}$-sets and double-pushout rewriting(DPO), our proposed representation can effectively handle structured knowledgeabout world states that support domain abstractions at all levels. Itformalizes the semantics of predicates according to a user-provided ontologyand preserves the semantics when transitioning between world states. Thismethod provides a formal semantics for using knowledge graphs and relationaldatabases to model world states and updates in planning. In this paper, weconceptually compare our category-theoretic representation with the classicalplanning representation. We show that our proposed representation hasadvantages over the classical representation in terms of handling implicitpreconditions and effects, and provides a more structured framework in which tomodel and solve planning problems.

A Categorical Representation Language and Computational System for Knowledge-Based Planning