Machine Understanding

Machine Understanding is a theoretical construct in the philosophy of artificial intelligence that characterizes the capacity of a computational system to represent, reason about, and derive meaning from information in a manner functionally analogous to human cognitive comprehension.

199

Publications

15.8K

Citations

390

Authors

188

Institutions

Publications per year

2017–2026

69

Authors

390

  1. AS
    Aaron Sloman

    University of Birmingham, United Kingdom

    3 publications · 124 citations

    3

    h-index

  2. MP
    Maja Pantić

    Imperial College London, United Kingdom

    3 publications · 305 citations

    3

    h-index

  3. AS
    Amit Sheth

    Wright State University, United States

    3 publications · 144 citations

    3

    h-index

  4. TS
    Thomas S. Huang

    University of Illinois Urbana-Champaign, United States

    3 publications · 305 citations

    3

    h-index

  5. YW
    Yingxu Wang

    University of Calgary, Canada

    3 publications · 65 citations

    3

    h-index

1–5 of 390

Venues

  1. Choice Reviews Online

    CRO · 8 publications · 1.8K citations

    8

    h-index

  2. arXiv (Cornell University)

    Repository · 8 publications · 121 citations

    8

    h-index

  3. AI & Society

    4 publications · 248 citations

    4

    h-index

  4. AI Magazine

    3 publications · 252 citations

    3

    h-index

  5. Cognitive Computation

    3 publications · 102 citations

    3

    h-index

1–5