Hierarchical Task Networks

Hierarchical Task Networks is a planning formalism in artificial intelligence that represents complex tasks as a hierarchy of decomposable actions, where high-level tasks are recursively broken down into simpler subtasks until primitive executable actions are reached.

144

Publications

8.8K

Citations

303

Authors

133

Institutions

Publications per year

2017–2026

41

Variants

Htns

Authors

303

  1. DN
    Dana Nau

    University of Maryland, College Park, United States

    18 publications · 3.3K citations

    17

    h-index

  2. HM
    Héctor Muñoz‐Avila

    Lehigh University, United States

    12 publications · 505 citations

    11

    h-index

  3. UK
    Ugur Kuter

    University of Maryland, College Park, United States

    10 publications · 386 citations

    10

    h-index

  4. DW
    David W. Aha

    United States Naval Research Laboratory, United States

    9 publications · 342 citations

    9

    h-index

  5. PB
    Pascal Bercher

    Universität Ulm, Germany

    8 publications · 295 citations

    8

    h-index

1–5 of 303

Venues

  1. 6

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 128 citations

    5

    h-index

  3. IEEE Intelligent Systems

    4 publications · 491 citations

    4

    h-index

  4. 4

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 148 citations

    4

    h-index

1–5