Traditional approaches to task planning assume that the planner has access to all of the world information needed to develop a complete, correct plan which can then be executed in its entirety by an agent. Since this assumption does not typically hold in realistic domains, we have implemented a planner which can plan to perform sensor operations to allow an agent to gather the information necessary to complete planning and achieve its goals in the face of missing or uncertain environmental information. Naturally this approach requires some execution to be interleaved with the planning process. In this paper we present the results of a systematic experimental study of this planner's performance under various conditions. The chief difficulty arises when the agent performs actions which interfere with or, in the worst case, preclude the possibility of the achievement of its later goals. We have found that by making intelligent decisions about goal ordering, what to sense, and when to sense it, the planner can significantly reduce the risk of committing to premature action. We have studied the problem both from the perspective of reversible and irreversible actions.