  Scheduling in the factory setting is compounded by computational complexity
and temporal uncertainty. Together, these two factors guarantee that the
process of constructing an optimal schedule will be costly and the chances of
executing that schedule will be slight. Temporal uncertainty in the task
execution time can be offset by several methods: eliminate uncertainty by
careful engineering, restore certainty whenever it is lost, reduce the
uncertainty by using more accurate sensors, and quantify and circumscribe the
remaining uncertainty. Unfortunately, these methods focus exclusively on the
sources of uncertainty and fail to apply knowledge of the tasks which are to be
scheduled. A complete solution must adapt the schedule of activities to be
performed according to the evolving state of the production world. The example
of vision-directed assembly is presented to illustrate that the principle of
least commitment, in the creation of a plan, in the representation of a
schedule, and in the execution of a schedule, enables a robot to operate
intelligently and efficiently, even in the presence of considerable uncertainty
in the sequence of future events.
