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Action Constraints for Planning

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Recent Advances in AI Planning (ECP 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1809))

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Abstract

Recent progress in the applications of propositional planning systems has led to an impressive speed-up of solution time and an increase in tractable problem size. In part, this improvement stems from the use of domain-dependent knowledge in form of state constraints. In this paper we introduce a different class of constraints: action constraints . They express domain-dependent knowledge about the use of actions in solution plans and can express strategies which are used by human planners. The use of action constraints results in a tendency to better plans. We explain how to calculate and apply action constraints in the framework of parallel total-order planning, which is the design of the most powerful planners at the moment. We present two classes of action constraints and demonstrate their capabilities in the planner ProbaPla.

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Authors and Affiliations

  1. Darmstadt University of Technology, Alexanderstraße 10, 64283, Darmstadt, Germany

    Ulrich Scholz

Authors
  1. Ulrich Scholz
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Editor information

Editors and Affiliations

  1. Institute for Artificial Intelligence, Ulm University, Germany

    Susanne Biundo

  2.  ,  

    Maria Fox

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© 2000 Springer-Verlag Berlin Heidelberg

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Scholz, U. (2000). Action Constraints for Planning. In: Biundo, S., Fox, M. (eds) Recent Advances in AI Planning. ECP 1999. Lecture Notes in Computer Science(), vol 1809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720246_12

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  • DOI: https://doi.org/10.1007/10720246_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67866-3

  • Online ISBN: 978-3-540-44657-6

  • eBook Packages: Springer Book Archive

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