Maintaining consistency in a robot's knowledge-base via diagnostic reasoning

Non-deterministic reality is a severe challenge for autonomous robots. Malfunctioning actions, inaccurate sensor perception and exogenous events easily lead to inconsistencies between an actual situation and the internal knowledge-base encoding a robot's belief. For a viable reasoning in dynamic environments, a robot is thus required to efficiently cope with such inconsistencies and maintain a consistent knowledge-base as fundament for its decision-making. In this paper, we present a belief management system based on the well-known agent programming language IndiGolog and history-based diagnosis. Extending the language's default mechanisms, we add a belief management system that is capable of handling several fault types that lead to belief inconsistencies. First experiments in the domain of service robots show the effectiveness of our approach.