Memory-bounded Search

Memory-Bounded Search is a heuristic search strategy that belongs to the class of informed search algorithms, distinguished by its explicit constraint on memory usage during node expansion, typically employing mechanisms to discard or summarize less promising paths when memory limits are reached.

81

Publications

12.9K

Citations

196

Authors

113

Institutions

Publications per year

2017–2026

14

Authors

196

  1. RD
    Rina Dechter

    University of California, Irvine, United States

    5 publications · 119 citations

    5

    h-index

  2. AF
    Ariel Felner

    Ben-Gurion University of the Negev, Israel

    5 publications · 294 citations

    5

    h-index

  3. AP
    Andrzej Pelc

    Université du Québec en Outaouais, Canada

    4 publications · 133 citations

    4

    h-index

  4. VB
    Vadim Bulitko

    University of Alberta, Canada

    3 publications · 36 citations

    3

    h-index

  5. RM
    Radu Marinescu

    University of California, Irvine, United States

    3 publications · 60 citations

    3

    h-index

1–5 of 196

Venues

  1. International Joint Conference on Artificial Intelligence

    Conference · 3 publications · 247 citations

    3

    h-index

  2. Journal of Artificial Intelligence Research

    JAIR · 3 publications · 228 citations

    3

    h-index

  3. ICGA Journal

    2 publications · 33 citations

    2

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 56 citations

    2

    h-index

  5. 2

    h-index

1–5