In our work on mobile robot control, we have adopted a decision-theoretic approach, encoding the problem in a belief network, a probabilistic model of those aspects of the world relevant to the goals of the robot. Belief networks provide a convenient way of modeling uncertain information and faulty sensors. In [Kirman et M., 1991] we show how they can be used to deal with sensors that provide only probabilistic information. For example, a vision system used to identify a target may provide the probability that the target is present in the field of view. Our work has been primarily with temporal belief networks; an extension of belief networks that allow the representation of change over time.