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This paper describes an architecture that combines the complementarystrengths of declarative programming and probabilistic graphical models toenable robots to represent, reason with, and learn from, qualitative andquantitative descriptions of uncertainty and knowledge. An action language isused for the low-level (LL) and high-level (HL) system descriptions in thearchitecture, and the definition of recorded histories in the HL is expanded toallow prioritized defaults. For any given goal, tentative plans created in theHL using default knowledge and commonsense reasoning are implemented in the LLusing probabilistic algorithms, with the corresponding observations used toupdate the HL history. Tight coupling between the two levels enables automaticselection of relevant variables and generation of suitable action policies inthe LL for each HL action, and supports reasoning with violation of defaults,noisy observations and unreliable actions in large and complex domains. Thearchitecture is evaluated in simulation and on physical robots transportingobjects in indoor domains; the benefit on robots is a reduction in taskexecution time of 39% compared with a purely probabilistic, but stillhierarchical, approach.

KR$^3$: An Architecture for Knowledge Representation and Reasoning in Robotics