TY - GEN
T1 - Fairness and Optimization in Dynamic Multiagent Allocation Problems
AU - Trabelsi, Yohai
N1 - Publisher Copyright:
© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - In many allocation problems, understanding individual agents' needs, wants, and tradeoffs is crucial for providing fair and efficient solutions. This paper begins with motivating applications and critical definitions. We review existing results, such as advising agents on relaxing restrictions for improved resource allocation, optimizing task allocation in online settings without rejection of a task, and more. We conclude by outlining three potential directions for future research.
AB - In many allocation problems, understanding individual agents' needs, wants, and tradeoffs is crucial for providing fair and efficient solutions. This paper begins with motivating applications and critical definitions. We review existing results, such as advising agents on relaxing restrictions for improved resource allocation, optimizing task allocation in online settings without rejection of a task, and more. We conclude by outlining three potential directions for future research.
UR - http://www.scopus.com/inward/record.url?scp=85204312081&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2024/973
DO - 10.24963/ijcai.2024/973
M3 - Conference contribution
AN - SCOPUS:85204312081
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 8516
EP - 8517
BT - Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
A2 - Larson, Kate
PB - International Joint Conferences on Artificial Intelligence
T2 - 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Y2 - 3 August 2024 through 9 August 2024
ER -