Natural hierarchical planning using operator decomposition

  • Maria Fox
Conference paper

DOI: 10.1007/3-540-63912-8_86

Volume 1348 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Fox M. (1997) Natural hierarchical planning using operator decomposition. In: Steel S., Alami R. (eds) Recent Advances in AI Planning. ECP 1997. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 1348. Springer, Berlin, Heidelberg

Abstract

Three approaches to hierarchical planning have been widely discussed in the recent planning literature: Hierarchical Task Network (HTN) decomposition, model-reduction and operator decomposition. Abstraction is used in different ways in these three approaches and this has significance for both efficiency and expressive power. This paper identifies four issues that arise in the use of abstraction in planning which have been treated in different ways in the three approaches identified above. These issues are discussed with reference to an approach to abstraction which combines elements of the HTN and operator-decomposition approaches. Particular comparison is made with the HTN approach in order to highlight some important distinctions between the task decomposition and operator decomposition planning strategies. The CNF (Common Normal Form) case study, used by Erol to demonstrate certain features of the HTN approach, is used as the basis for this comparison.

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Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Maria Fox
    • 1
  1. 1.Department of Computer ScienceUniversity of DurhamDurhamUK