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Robots assisting humans in complex domains have to represent knowledge andreason at both the sensorimotor level and the social level. The architecturedescribed in this paper couples the non-monotonic logical reasoningcapabilities of a declarative language with probabilistic belief revision,enabling robots to represent and reason with qualitative and quantitativedescriptions of knowledge and degrees of belief. Specifically, incompletedomain knowledge, including information that holds in all but a few exceptionalsituations, is represented as a Answer Set Prolog (ASP) program. The answer setobtained by solving this program is used for inference, planning, and forjointly 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 executedprobabilistically. The subset of the domain relevant to the action isidentified automatically, and observations extracted from sensor inputs performincremental Bayesian updates to a belief distribution defined over this domainsubset, with highly probable beliefs being committed to the ASP program. Thearchitecture's capabilities are illustrated in simulation and on a mobile robotin the context of a robot waiter operating in the dining room of a restaurant.

Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics