  This paper describes an architecture for robots that combines the
complementary strengths of probabilistic graphical models and declarative
programming to represent and reason with logic-based and probabilistic
descriptions of uncertainty and domain knowledge. An action language is
extended to support non-boolean fluents and non-deterministic causal laws. This
action language is used to describe tightly-coupled transition diagrams at two
levels of granularity, with a fine-resolution transition diagram defined as a
refinement of a coarse-resolution transition diagram of the domain. The
coarse-resolution system description, and a history that includes (prioritized)
defaults, are translated into an Answer Set Prolog (ASP) program. For any given
goal, inference in the ASP program provides a plan of abstract actions. To
implement each such abstract action, the robot automatically zooms to the part
of the fine-resolution transition diagram relevant to this action. A
probabilistic representation of the uncertainty in sensing and actuation is
then included in this zoomed fine-resolution system description, and used to
construct a partially observable Markov decision process (POMDP). The policy
obtained by solving the POMDP is invoked repeatedly to implement the abstract
action as a sequence of concrete actions, with the corresponding observations
being recorded in the coarse-resolution history and used for subsequent
reasoning. The architecture is evaluated in simulation and on a mobile robot
moving objects in an indoor domain, to show that it supports reasoning with
violation of defaults, noisy observations and unreliable actions, in complex
domains.
