  Robots assisting humans in complex domains have to represent knowledge and
reason at both the sensorimotor level and the social level. The architecture
described in this paper couples the non-monotonic logical reasoning
capabilities of a declarative language with probabilistic belief revision,
enabling robots to represent and reason with qualitative and quantitative
descriptions of knowledge and degrees of belief. Specifically, incomplete
domain knowledge, including information that holds in all but a few exceptional
situations, is represented as a Answer Set Prolog (ASP) program. The answer set
obtained by solving this program is used for inference, planning, and for
jointly explaining (a) unexpected action outcomes due to exogenous actions and
(b) partial scene descriptions extracted from sensor input. For any given task,
each action in the plan contained in the answer set is executed
probabilistically. The subset of the domain relevant to the action is
identified automatically, and observations extracted from sensor inputs perform
incremental Bayesian updates to a belief distribution defined over this domain
subset, with highly probable beliefs being committed to the ASP program. The
architecture's capabilities are illustrated in simulation and on a mobile robot
in the context of a robot waiter operating in the dining room of a restaurant.
