@inproceedings{72ad9ab70cdd42bc8db0eaedd2cf076c,
title = "Multiagent Learning for Competitive Opinion Optimization (Extended Abstract)",
abstract = "From a perspective of designing or engineering for opinion formation games in social networks, the opinion maximization (or minimization) problem has been studied mainly for designing subset selecting algorithms. We define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the leader minimize the sum of expressed opinions by doing so-called “internal opinion design”, knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We furthermore consider multiagent learning, specifically using the Optimistic Gradient Descent Ascent, and analyze its convergence to equilibria in the simultaneous version of competitive opinion optimization.",
keywords = "Competitive opinion optimization, Multiagent learning, Optimistic gradient descent ascent",
author = "Chen, {Po An} and Lu, {Chi Jen} and Lin, {Chuang Chieh} and Fu, {Ke Wei}",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022 ; Conference date: 15-12-2022 Through 17-12-2022",
year = "2022",
doi = "10.1007/978-981-19-9582-8_6",
language = "English",
isbn = "9789811995811",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "61--72",
editor = "Sun-Yuan Hsieh and Ling-Ju Hung and Sheng-Lung Peng and Ralf Klasing and Chia-Wei Lee",
booktitle = "New Trends in Computer Technologies and Applications - 25th International Computer Symposium, ICS 2022, Proceedings",
address = "德國",
}