Automata Learning

Automata Learning is a computational method that systematically infers the structure of a finite automaton from observed input-output behaviors, distinguishing itself from general machine learning by leveraging formal language theory to ensure the learned model precisely captures the regularities of the target system.

714

Publications

33.5K

Citations

1.3K

Authors

554

Institutions

Publications per year

2017–2026

144

Authors

1.3K

  1. MR
    Mohammad Reza Meybodi

    Amirkabir University of Technology, Iran

    35 publications · 1.2K citations

    21

    h-index

  2. BJ
    B. John Oommen

    Carleton University, Canada

    30 publications · 1.4K citations

    19

    h-index

  3. MT
    M.A.L. Thathachar

    Indian Institute of Science Bangalore, India

    17 publications · 1.6K citations

    15

    h-index

  4. CL
    C. Lee Giles

    Pennsylvania State University, United States

    17 publications · 1.1K citations

    13

    h-index

  5. JA
    Javad Akbari Torkestani

    Islamic Azad University, Arak, Iran

    13 publications · 489 citations

    13

    h-index

1–5 of 1.3K

Venues

  1. IEEE Transactions on Systems Man and Cybernetics

    30 publications · 2.6K citations

    21

    h-index

  2. Machine Learning

    22 publications · 1.4K citations

    18

    h-index

  3. 16

    h-index

  4. arXiv (Cornell University)

    Repository · 20 publications · 468 citations

    13

    h-index

  5. Theoretical Computer Science

    13 publications · 349 citations

    12

    h-index

1–5