Learning hedonic games via probabilistic topic modelingLearning hedonic games via probabilistic topic modeling
Πλήρης Δημοσίευση σε Συνέδριο
Conference Full Paper
2020-10-262018enA usual assumption in the hedonic games literature is that of complete information; however, in the real world this is almost never the case. As such, in this work we assume that the players’ preference relations are hidden: players interact within an unknown hedonic game, of which they can observe a small number of game instances. We adopt probabilistic topic modeling as a learning tool to extract valuable information from the sampled game instances. Specifically, we employ the online Latent Dirichlet Allocation (LDA) algorithm in order to learn the latent preference relations in Hedonic Games with Dichotomous preferences. Our simulation results confirm the effectiveness of our approach.http://creativecommons.org/licenses/by/4.0/62-7616th European Conference on Multi-Agent SystemsMulti-Agent Systems
Georgara Athina
Γεωργαρα Αθηνα
Ntiniakou Thaleia
Ντινιακου Θαλεια
Chalkiadakis Georgios
Χαλκιαδακης Γεωργιος
Springer Nature
Adaptation and learning
Cooperative game theory
Hedonic games