Constraint-driven Generation

Constraint-Driven Generation is a generative methodology in which the production of outputs is systematically governed by predefined constraints that restrict the solution space to ensure adherence to specific structural, semantic, or functional requirements.

110

Publications

3.6K

Citations

302

Authors

124

Institutions

Publications per year

2017–2026

36

Authors

302

  1. AS
    Alberto Sangiovanni‐Vincentelli

    University of California, Berkeley, United States

    4 publications · 141 citations

    4

    h-index

  2. MP
    Matthew P. McCurdy

    University of Illinois Chicago, United States

    4 publications · 110 citations

    4

    h-index

  3. ED
    Eric D. Leshikar

    University of Illinois Chicago, United States

    4 publications · 110 citations

    4

    h-index

  4. FP
    François Pachet

    Sony (Taiwan), Taiwan

    3 publications · 175 citations

    3

    h-index

  5. KA
    Ken Albin

    Motorola (United States), United States

    3 publications · 94 citations

    3

    h-index

1–5 of 302

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 78 citations

    3

    h-index

  2. IEEE Transactions on Sustainable Energy

    TSTE · 3 publications · 122 citations

    3

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 39 citations

    2

    h-index

  4. 2

    h-index

  5. International Joint Conference on Artificial Intelligence

    Conference · 2 publications · 102 citations

    2

    h-index

1–5