Probabilistic Action Representation

Probabilistic Action Representation is a formal framework in cognitive science and artificial intelligence that models actions as probability distributions over possible outcomes, incorporating uncertainty in action selection and execution within dynamic environments.

112

Publications

7K

Citations

297

Authors

130

Institutions

Publications per year

2017–2026

24

Authors

297

  1. AB
    Aaron Bobick

    Georgia Institute of Technology, United States

    4 publications · 355 citations

    4

    h-index

  2. HK
    Henry Kautz

    University of Rochester, United States

    3 publications · 122 citations

    3

    h-index

  3. KY
    Kristina Yordanova

    University of Rostock, Germany

    3 publications · 55 citations

    3

    h-index

  4. GN
    Gerhard Neumann

    Technische Universität Darmstadt, Germany

    3 publications · 173 citations

    3

    h-index

  5. JP
    Jan Peters

    Technische Universität Darmstadt, Germany

    3 publications · 173 citations

    3

    h-index

1–5 of 297

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 192 citations

    7

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 359 citations

    4

    h-index

  3. Journal of Artificial Intelligence Research

    JAIR · 4 publications · 332 citations

    4

    h-index

  4. Robotics and Autonomous Systems

    2 publications · 60 citations

    2

    h-index

  5. National Conference on Artificial Intelligence

    Conference · 2 publications · 103 citations

    2

    h-index

1–5