Pomdp Solvers

POMDP Solver is a computational method designed to determine optimal policies for decision-making under uncertainty in partially observable environments by solving the underlying partially observable Markov decision process model through exact or approximate inference techniques.

204

Publications

13.8K

Citations

391

Authors

165

Institutions

Publications per year

2017–2026

56

Variants

Partially Observable Markov Decision Process Solvers

Authors

391

  1. GS
    Guy Shani

    Ben-Gurion University of the Negev, Israel

    11 publications · 694 citations

    11

    h-index

  2. SZ
    Shlomo Zilberstein

    University of Massachusetts Amherst, United States

    11 publications · 884 citations

    11

    h-index

  3. MJ
    Mykel J. Kochenderfer

    Stanford University, United States

    8 publications · 522 citations

    8

    h-index

  4. DH
    David Hsu

    National University of Singapore, Singapore

    8 publications · 1K citations

    8

    h-index

  5. CA
    Christopher Amato

    Massachusetts Institute of Technology, United States

    7 publications · 224 citations

    7

    h-index

1–5 of 391

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 13 publications · 414 citations

    12

    h-index

  2. arXiv (Cornell University)

    Repository · 13 publications · 1K citations

    11

    h-index

  3. 7

    h-index

  4. The International Journal of Robotics Research

    IJRR · 5 publications · 509 citations

    5

    h-index

  5. Autonomous Agents and Multi-Agent Systems

    5 publications · 585 citations

    5

    h-index

1–5