@inproceedings{f6ed58d680db4362b52fb93c9302c336,
title = "Online Friends Partitioning Under Uncertainty",
abstract = "We study the friendship-based online coalition formation problem, in which agents that appear one at a time should be partitioned into coalitions, and an agent{\textquoteright}s utility for a coalition is the number of her neighbors (i.e., friends) within the coalition.Unlike prior work, agents{\textquoteright} friendships may be uncertain.We analyze the desirability of the resulting partition in the common term of optimality, aiming to maximize the social welfare.We design an online algorithm termed Maximum Predicted Coalitional Friends (MPCF), which is enhanced with predictions of each agent{\textquoteright}s number of friends within any possible coalition.For common classes of random graphs, we prove that MPCF is optimal, and, for certain graphs, provides the same guarantee as the best known competitive algorithm for settings without uncertainty.",
author = "Saar Cohen and Noa Agmon",
note = "Publisher Copyright: {\textcopyright} 2024 The Authors.; 27th European Conference on Artificial Intelligence, ECAI 2024 ; Conference date: 19-10-2024 Through 24-10-2024",
year = "2024",
month = oct,
day = "16",
doi = "10.3233/FAIA240882",
language = "الإنجليزيّة",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "3332--3339",
editor = "Ulle Endriss and Melo, {Francisco S.} and Kerstin Bach and Alberto Bugarin-Diz and Alonso-Moral, {Jose M.} and Senen Barro and Fredrik Heintz",
booktitle = "ECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings",
address = "هولندا",
}