Opponent Modeling

Opponent Modeling is a computational framework in game-playing research that involves constructing and updating predictive models of an opponent's strategies, intentions, or decision-making processes to inform and optimize one's own actions within competitive interactions.

221

Publications

13.5K

Citations

526

Authors

215

Institutions

Publications per year

2017–2026

51

Authors

526

  1. MV
    Manuela Veloso

    Carnegie Mellon University, United States

    6 publications · 192 citations

    6

    h-index

  2. MB
    Michael Bowling

    University of Alberta, Canada

    6 publications · 324 citations

    6

    h-index

  3. PR
    Patrick Riley

    Carnegie Mellon University, United States

    5 publications · 143 citations

    5

    h-index

  4. KV
    Koen V. Hindriks

    Delft University of Technology, Netherlands

    5 publications · 431 citations

    5

    h-index

  5. PC
    Peter Cowling

    University of Bradford, United Kingdom

    5 publications · 127 citations

    5

    h-index

1–5 of 526

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 361 citations

    10

    h-index

  2. 7

    h-index

  3. National Conference on Artificial Intelligence

    Conference · 5 publications · 106 citations

    5

    h-index

  4. Journal of the Optical Society of America A

    5 publications · 3.8K citations

    5

    h-index

  5. ICGA Journal

    5 publications · 131 citations

    5

    h-index

1–5