  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.
