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Learning hedonic games via probabilistic topic modeling

Georgara Athina, Ntiniakou Thaleia, Chalkiadakis Georgios

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Extent15 pagesen
TitleLearning hedonic games via probabilistic topic modelingen
CreatorGeorgara Athinaen
CreatorΓεωργαρα Αθηναel
CreatorNtiniakou Thaleiaen
CreatorΝτινιακου Θαλειαel
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
PublisherSpringer Natureen
Content SummaryA 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.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Date of Item2020-10-26-
Date of Publication2018-
SubjectAdaptation and learningen
SubjectCooperative game theoryen
SubjectHedonic gamesen
Bibliographic CitationA. 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_5en