  The new field of Explainable Planning (XAIP) has produced a variety of
approaches to explain and describe the behavior of autonomous agents to human
observers. Many summarize agent behavior in terms of the constraints, or
''rules,'' which the agent adheres to during its trajectories. In this work, we
narrow the focus from summary to specific moments in individual trajectories,
offering a ''pointwise-in-time'' view. Our novel framework, which we define on
Linear Temporal Logic (LTL) rules, assigns an intuitive status to any rule in
order to describe the trajectory progress at individual time steps; here, a
rule is classified as active, satisfied, inactive, or violated. Given a
trajectory, a user may query for status of specific LTL rules at individual
trajectory time steps. In this paper, we present this novel framework, named
Rule Status Assessment (RSA), and provide an example of its implementation. We
find that pointwise-in-time status assessment is useful as a post-hoc
diagnostic, enabling a user to systematically track the agent's behavior with
respect to a set of rules.
