Multi-agent Action Representation

Multi-Agent Action Representation is a formal framework within action representation that specifies how actions of multiple interacting agents are modeled, structured, and interpreted in joint task environments, distinguishing itself by accounting for interdependencies, coordination, and decentralized decision-making among agents.

42

Publications

2.3K

Citations

95

Authors

62

Institutions

Publications per year

2017–2026

7

Authors

95

  1. MP
    Munindar P. Singh

    North Carolina State University, United States

    3 publications · 57 citations

    3

    h-index

  2. MG
    Michael Georgeff

    SRI International, United States

    2 publications · 132 citations

    2

    h-index

  3. AB
    Aaron Bobick

    Georgia Institute of Technology, United States

    2 publications · 190 citations

    2

    h-index

  4. AB
    Andraž Bežek

    Jožef Stefan Institute, Slovenia

    2 publications · 35 citations

    2

    h-index

  5. RI
    Ronen I. Brafman

    Ben-Gurion University of the Negev, Israel

    2 publications · 141 citations

    2

    h-index

1–5 of 95

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 122 citations

    3

    h-index

  2. National Conference on Artificial Intelligence

    Conference · 2 publications · 488 citations

    2

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. International Joint Conference on Artificial Intelligence

    Conference · 1 publications · 21 citations

    1

    h-index

1–5