Generalized Discrete Preference Games
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 53-58.
Abstract
Recently, much attention has been devoted to discrete preference games to model the formation of opinions in social networks. More specifically, these games model the agents' strategic decision of expressing publicly an opinion, which is a result of an interplay between the agent's private belief and the social pressure. However, these games have very limited expressive power; they can model only very simple social relations and they assume that all the agents respond to social pressure in the same way. In this paper, we define and study the novel class of generalized discrete preference games. These games have additional characteristics that can model social relations to allies or competitors and complex relations among more than two agents. Moreover, they introduce different levels of strength for each relation, and they personalize the dependence of each agent to her neighborhood.
@inproceedings{ijcai2016p15,
title = {Generalized Discrete Preference Games},
author = {Auletta, Vincenzo and Caragiannis, Ioannis and Ferraioli, Diodato and Galdi, Clemente and Persiano, Giuseppe},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {53--58},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/15},
}
TY - CPAPER TI - Generalized Discrete Preference Games AU - Auletta, Vincenzo AU - Caragiannis, Ioannis AU - Ferraioli, Diodato AU - Galdi, Clemente AU - Persiano, Giuseppe T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 53 EP - 58 UR - https://ijcai.org/proceedings/2016/15 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: