A decision-theoretic framework for integrating sensors into AI plans

Strategic planners for robots that survive in a realistic environment must coordinate sensor acquisition with robotic activity. Sensory coordination is discussed as a decision problem. It is assumed that although relevant sensory data can generally be requested, costs prohibit many of these requests from being granted. The underlying question is whether a central controller should grant or deny a system´s request for a sensory update. Given this fundamental problem, a decision-theoretic analysis is used to derive closed-form formulas for the appropriate frequency of sensor integration as a function of parameters of the equipment, the domain, and the types of errors from which the system must recover. The derived formulas give precise descriptions of the frequency with which sensory requests should be granted in a simplified, formalized setting. They also serve as a template into which a variety of real-world parameterizations could fit