  In this paper, we view a policy or plan as a transition system over a space
of information states that reflect a robot's or other observer's perspective
based on limited sensing, memory, computation, and actuation. Regardless of
whether policies are obtained by learning algorithms, planning algorithms, or
human insight, we want to know the limits of feasibility for given robot
hardware and tasks. Toward the quest to find the best policies, we establish in
a general setting that minimal information transition systems (ITSs) exist up
to reasonable equivalence assumptions, and are unique under some general
conditions. We then apply the theory to generate new insights into several
problems, including optimal sensor fusion/filtering, solving basic planning
tasks, and finding minimal representations for feasible policies.
