  The architecture described in this paper encodes a theory of intentions based
on the the key principles of non-procrastination, persistence, and
automatically limiting reasoning to relevant knowledge and observations. The
architecture reasons with transition diagrams of any given domain at two
different resolutions, with the fine-resolution description defined as a
refinement of, and hence tightly-coupled to, a coarse-resolution description.
Non-monotonic logical reasoning with the coarse-resolution description computes
an activity (i.e., plan) comprising abstract actions for any given goal. Each
abstract action is implemented as a sequence of concrete actions by
automatically zooming to and reasoning with the part of the fine-resolution
transition diagram relevant to the current coarse-resolution transition and the
goal. Each concrete action in this sequence is executed using probabilistic
models of the uncertainty in sensing and actuation, and the corresponding
fine-resolution outcomes are used to infer coarse-resolution observations that
are added to the coarse-resolution history. The architecture's capabilities are
evaluated in the context of a simulated robot assisting humans in an office
domain, on a physical robot (Baxter) manipulating tabletop objects, and on a
wheeled robot (Turtlebot) moving objects to particular places or people. The
experimental results indicate improvements in reliability and computational
efficiency compared with an architecture that does not include the theory of
intentions, and an architecture that does not include zooming for
fine-resolution reasoning.
