Story validation and approximate path inference with a sparse network of heterogeneous sensors

Given a story from an agent (sensor outputs from a robot or a tale told by a human) and recordings from a spare network of heterogeneous sensors, this paper provides efficient algorithms that validate whether it is possible to reconstruct a path compatible with the sensor recordings that is also "close" to the agent\´s story. In solving the proposed problems, we show that effective exploitation of a unique finite automaton structure yields time complexity linear in both the length of the story and the length of the sensor observation history. Besides immediate applicability towards security and forensics problems, the idea of behavior validation using external sensors also appears promising in complementing design time model verification.