Classical planners have traditionally made the closed world assumption -- facts absent from the planner’s world model are false. Incomplete-information planners make the open world assumption -- the truth value of a fact absent from the planner’s model is unknown, and must be sensed. The open world assump: tion leads to two difficulties: (1) How can the planner determine the scope of a universally quantified goal? (2) When is a sensory action redundant, yielding information already known to the planner? This paper describes the fully-implemented xII planner which solves both problems by representing and reasoning about local closed world information (LOW). We report on experiments utilizing the UNIX softbot (software robot) which demonstrate that LCW can substantially improve the softbot’s performance by eliminating redundant information gathering.