  In studying robots and planning problems, a basic question is what is the
minimal information a robot must obtain to guarantee task completion. Erdmann's
theory of action-based sensors is a classical approach to characterizing
fundamental information requirements. That approach uses a plan to derive a
type of virtual sensor which prescribes actions that make progress toward a
goal. We show that the established theory is incomplete: the previous method
for obtaining such sensors, using backchained plans, overlooks some sensors.
Furthermore, there are plans, that are guaranteed to achieve goals, where the
existing methods are unable to provide any action-based sensor. We identify the
underlying feature common to all such plans. Then, we show how to produce
action-based sensors even for plans where the existing treatment is inadequate,
although for these cases they have no single canonical sensor. Consequently,
the approach is generalized to produce sets of sensors. Finally, we show also
that this is a complete characterization of action-based sensors for planning
problems and discuss how an action-based sensor translates into the traditional
conception of a sensor.
