Encoding Compositionality in Classical Planning Solutions
  Classical AI planners provide solutions to planning problems in the form of
long and opaque text outputs. To aid in the understanding transferability of
planning solutions, it is necessary to have a rich and comprehensible
representation for both human and computers beyond the current line-by-line
text notation. In particular, it is desirable to encode the trace of literals
throughout the plan to capture the dependencies between actions selected. The
approach of this paper is to view the actions as maps between literals and the
selected plan as a composition of those maps. The mathematical theory, called
category theory, provides the relevant structures for capturing maps, their
compositions, and maps between compositions. We employ this theory to propose
an algorithm agnostic, model-based representation for domains, problems, and
plans expressed in the commonly used planning description language, PDDL. This
category theoretic representation is accompanied by a graphical syntax in
addition to a linear notation, similar to algebraic expressions, that can be
used to infer literals used at every step of the plan. This provides the
appropriate constructive abstraction and facilitates comprehension for human
operators. In this paper, we demonstrate this on a plan within the Blocksworld
domain.
