Constraint Learning

Constraint Learning is a computational method within constraint satisfaction that identifies and incorporates new constraints from problem-solving experiences to refine the search space and improve reasoning efficiency.

135

Publications

5.6K

Citations

348

Authors

170

Institutions

Publications per year

2017–2026

40

Authors

348

  1. EC
    Eugene C. Freuder

    University of New Hampshire, United States

    6 publications · 144 citations

    6

    h-index

  2. CB
    Christian Bessière

    Centre National de la Recherche Scientifique, France

    5 publications · 163 citations

    5

    h-index

  3. LD
    Luc De Raedt

    KU Leuven, Belgium

    4 publications · 149 citations

    4

    h-index

  4. DR
    Dan Roth

    University of Illinois Urbana-Champaign, United States

    4 publications · 113 citations

    4

    h-index

  5. SK
    Samuel Kolb

    KU Leuven, Belgium

    3 publications · 78 citations

    3

    h-index

1–5 of 348

Venues

  1. Artificial Intelligence

    9 publications · 352 citations

    9

    h-index

  2. arXiv (Cornell University)

    Repository · 10 publications · 181 citations

    9

    h-index

  3. National Conference on Artificial Intelligence

    Conference · 3 publications · 109 citations

    3

    h-index

  4. 3

    h-index

  5. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 3 publications · 141 citations

    3

    h-index

1–5