Rule-based Adaptation

Rule-Based Adaptation is a methodological approach in adaptive systems that uses predefined conditional rules to dynamically modify interface or content elements in response to user interactions, distinguishing itself from other adaptation techniques by relying on explicit, human-defined logic rather than statistical or machine learning models.

106

Publications

4.4K

Citations

282

Authors

133

Institutions

Publications per year

2017–2026

19

Authors

282

  1. PA
    Plamen Angelov

    Lancaster University, United Kingdom

    4 publications · 376 citations

    4

    h-index

  2. GP
    Giuseppe Pozzi

    Politecnico di Milano, Italy

    3 publications · 42 citations

    3

    h-index

  3. DL
    David Leake

    Indiana University Bloomington, United States

    3 publications · 98 citations

    3

    h-index

  4. RB
    Richard Buswell

    Loughborough University, United Kingdom

    3 publications · 218 citations

    3

    h-index

  5. FD
    Florian Daniel

    University of Trento, Italy

    3 publications · 42 citations

    3

    h-index

1–5 of 282

Venues

  1. IEEE Transactions on Fuzzy Systems

    TFS · 5 publications · 306 citations

    5

    h-index

  2. Expert Systems with Applications

    4 publications · 176 citations

    4

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 106 citations

    3

    h-index

  4. International Journal of Intelligent Systems

    2 publications · 73 citations

    2

    h-index

  5. ACM Transactions on Autonomous and Adaptive Systems

    TAAS · 2 publications · 163 citations

    2

    h-index

1–5