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Learning Hedonic Games via Probabilistic Topic Modeling

  • Athina GeorgaraEmail author
  • Thalia Ntiniakou
  • Georgios Chalkiadakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11450)

Abstract

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.

Keywords

Adaptation and learning Cooperative game theory 

Notes

Acknowledgements

We thank Michalis Mamakos for code sharing.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Athina Georgara
    • 1
    Email author
  • Thalia Ntiniakou
    • 1
  • Georgios Chalkiadakis
    • 1
  1. 1.School of Electrical and Computer EngineeringTechnical University of CreteChaniaGreece

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