Interpretable Robot Decision-making

Interpretable Robot Decision-Making is a robotic decision-making process that belongs to the class of transparent autonomous systems and is distinguished by its capacity to generate internally coherent, human-comprehensible explanations of its choices, enabling observers to understand, predict, and trust the rationale behind specific actions.

9

Publications

267

Citations

33

Authors

19

Institutions

Publications per year

2017–2026

7

Authors

33

  1. AW
    Ariesta Wibisono Anditya

    University of Bengkulu, Indonesia

    1 publications · 16 citations

    1

    h-index

  2. SJ
    Shiyan Jiang

    North Carolina State University, United States

    1 publications · 12 citations

    1

    h-index

  3. ON
    Olfa Nasraoui

    University of Louisville, United States

    1 publications · 26 citations

    1

    h-index

  4. JS
    Jivko Sinapov

    Iowa State University, United States

    1 publications · 45 citations

    1

    h-index

  5. ME
    Moataz Eltoukhy

    University of Miami, United States

    1 publications · 12 citations

    1

    h-index

1–5 of 33

Venues

  1. Automatica

    1 publications · 52 citations

    1

    h-index

  2. 1

    h-index

  3. 1

    h-index

  4. Lex Scientia Law Review

    1 publications · 16 citations

    1

    h-index

  5. Journal for STEM Education Research

    1 publications · 12 citations

    1

    h-index

1–5