Planning research in Artificial Intelligence (AI) has often focused
on problems where there are cascading levels of action choice and
complex interactions between actions. In contrast. Scheduling
research has focused on much larger problems where there is little
action choice, but the resulting ordering problem is hard. In this
paper, we give an overview of M planning and scheduling techniques,
focusing on their similarities, differences, and limitations. We
also argue that many difficult practical problems lie somewhere
between planning and scheduling, and that neither area has the
right set of tools for solving these vexing problems.
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