Bridging the gap between planning and scheduling

  • Authors:

  • David E. Smith;Jeremy Frank;Ari K. Jónsson

  • Affiliations:

  • NASA Ames Research Center, Mail Stop 269-2, Moffett Field, CA 94035, USA. Email: {de2smith, frank, jonsson}@ptolemy.arc.nasa.gov;NASA Ames Research Center, Mail Stop 269-2, Moffett Field, CA 94035, USA. Email: {de2smith, frank, jonsson}@ptolemy.arc.nasa.gov;NASA Ames Research Center, Mail Stop 269-2, Moffett Field, CA 94035, USA. Email: {de2smith, frank, jonsson}@ptolemy.arc.nasa.gov

  • Venue:
  • The Knowledge Engineering Review
  • Year:
  • 2000

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Abstract

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 AI 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.