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Complexity results for HTN planning

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Abstract

Most practical work on AI planning systems during the last fifteen years has been based on Hierarchical Task Network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks, and how it compares to STRIPS-style planning.

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Authors and Affiliations

  1. Computer Science Department, Institute for Systems Research, and Institute for Advanced Computer Studies University of Maryland, 20742, College Park, MD, USA

    Kutluhan Erol, James Hendler & Dana S. Nau

Authors
  1. Kutluhan Erol
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  2. James Hendler
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  3. Dana S. Nau
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Additional information

This work was supported in part by NSF Grant NSFD CDR-88003012 to the Institute for Systems Research, and NSF grant IRI9306580 and ONR grant N00014-91-J-1451 to the Computer Science Department.

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Erol, K., Hendler, J. & Nau, D.S. Complexity results for HTN planning. Ann Math Artif Intell 18, 69–93 (1996). https://doi.org/10.1007/BF02136175

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