An approach to planning sensing strategies dynamically on the basis of the system's current best information about the world is described. The approach is for the system to propose a sensing operation automatically and then to determine the maximum ambiguity which might remain in the world description if that sensing operation were applied. When this maximum ambiguity is sufficiently small, the corresponding sensing operation is applied. To do this, the system formulates object hypotheses and assesses its relative belief in those hypotheses to predict what features might be observed by a proposed sensing operations. Furthermore, since the number of sensing operations available to the system can be arbitrarily large, equivalent sensing operations are grouped together using a data structure that is based on the aspect graph. In order to measure the ambiguity in a set of hypotheses, the concept of entropy from information theory is applied. This allows the determination of ambiguity in a hypothesis set in terms of the number of hypotheses and the system's distribution of belief among those hypotheses.