Hierarchical Task Planning

Hierarchical Task Planning is a computational framework in behavior planning that decomposes complex tasks into structured sequences of subtasks organized across multiple levels of abstraction, distinguishing it from flat or non-hierarchical approaches by explicitly representing task–subtask relationships in a top-down hierarchy.

184

Publications

11.9K

Citations

413

Authors

160

Institutions

Publications per year

2017–2026

60

Authors

413

  1. DN
    Dana Nau

    University of Maryland, College Park, United States

    20 publications · 3.3K citations

    17

    h-index

  2. UK
    Ugur Kuter

    University of Maryland, College Park, United States

    10 publications · 386 citations

    10

    h-index

  3. HM
    Héctor Muñoz‐Avila

    Lehigh University, United States

    10 publications · 372 citations

    10

    h-index

  4. DW
    David W. Aha

    United States Naval Research Laboratory, United States

    9 publications · 342 citations

    9

    h-index

  5. JH
    James Hendler

    University of Maryland, College Park, United States

    8 publications · 1.2K citations

    8

    h-index

1–5 of 413

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 264 citations

    9

    h-index

  2. 8

    h-index

  3. IEEE Robotics and Automation Letters

    6 publications · 237 citations

    6

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 196 citations

    6

    h-index

  5. National Conference on Artificial Intelligence

    Conference · 5 publications · 157 citations

    5

    h-index

1–5