Factored Pomdps

Factored POMDPs is a modeling framework for partially observable Markov decision processes in which the state and observation spaces are represented compactly using factored representations based on structured dependencies among state and observation variables.

26

Publications

1.3K

Citations

56

Authors

31

Institutions

Publications per year

2017–2026

0

Variants

Factored Partially Observable Markov Decision Processes

Authors

56

  1. MT
    Matthijs T. J. Spaan

    Delft University of Technology, Netherlands

    4 publications · 68 citations

    4

    h-index

  2. ZF
    Zhengzhu Feng

    University of Massachusetts Amherst, United States

    3 publications · 128 citations

    3

    h-index

  3. FA
    Frans A. Oliehoek

    University of Amsterdam, Netherlands

    2 publications · 106 citations

    2

    h-index

  4. GS
    Guy Shani

    Ben-Gurion University of the Negev, Israel

    2 publications · 55 citations

    2

    h-index

  5. EA
    Eric A. Hansen

    Mississippi State University, United States

    2 publications · 117 citations

    2

    h-index

1–5 of 56

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 183 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 98 citations

    2

    h-index

  3. Journal of Machine Learning Research

    JMLR · 1 publications · 10 citations

    1

    h-index

  4. 1

    h-index

  5. The International Journal of Robotics Research

    IJRR · 1 publications · 238 citations

    1

    h-index

1–5