State Abstraction

State Abstraction is a methodological construct in artificial intelligence that selectively retains relevant features of a state space while discarding irrelevant details to improve computational efficiency and generalization in decision-making processes.

158

Publications

8.1K

Citations

410

Authors

138

Institutions

Publications per year

2017–2026

49

Authors

410

  1. ML
    Michael L. Littman

    Rutgers, The State University of New Jersey, United States

    7 publications · 532 citations

    7

    h-index

  2. DA
    David Abel

    Brown University, United States

    7 publications · 291 citations

    7

    h-index

  3. DP
    Doina Precup

    McGill University, Canada

    4 publications · 94 citations

    5

    h-index

  4. MB
    Michael Bowling

    University of Alberta, Canada

    5 publications · 196 citations

    5

    h-index

  5. TG
    Thomas G. Dietterich

    Oregon State University, United States

    3 publications · 88 citations

    3

    h-index

1–5 of 410

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 243 citations

    8

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 231 citations

    7

    h-index

  3. Journal of Artificial Intelligence Research

    JAIR · 6 publications · 1.7K citations

    6

    h-index

  4. Artificial Intelligence

    5 publications · 241 citations

    5

    h-index

  5. 4

    h-index

1–5