![Ιδρυματικό Αποθετήριο [SANDBOX]](images/dias.logo.png "Ιδρυματικό Αποθετήριο [SANDBOX]"){kind=logo}
Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/65F99335-CED0-4588-8BFB-631FBA388861 | ||
| Έτος | 2018 | ||
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | ||
| Άδεια Χρήσης |
|
||
| Βιβλιογραφική Αναφορά | A. Georgara, T. Ntiniakou and G. Chalkiadakis, "Learning hedonic games via probabilistic topic modeling," in Multi-Agent Systems, vol. 11450, Lecture Notes in Computer Science, M. Slavkovik, Ed., Cham, Switzerland: Springer Nature, 2019, pp. 62-76. doi: 10.1007/978-3-030-14174-5_5 https://doi.org/10.1007/978-3-030-14174-5_5 | ||
| Εμφανίζεται στις Συλλογές |
A 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.
Copyright © DIAS 2013
