Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Temporal Epistemic Gossip Problems Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Learning Hedonic Games via Probabilistic Topic Modeling

Authors : Athina Georgara, Thalia Ntiniakou, Georgios Chalkiadakis

Published in: Multi-Agent Systems

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Interpreting Information in Smart Environments with Social Patterns
next chapter Learning Best Response Strategies for Agents in Ad Exchanges
Appendix
Available only for authorised users
Footnotes
1
We have preliminary results showing our approach can be quite effective in even more complex settings.
 
2
The number of agents participating in each \(\varvec{\phi }_i\), along with the total number of formulae per agent within each level of complexity environment were chosen so that the required dataset could be generated within a reasonable time frame; these numbers do not impose any burden on the LDA algorithm itself.
 
3
For practical reasons, the logged information is repeated more than once within a document. That is, we boost the term frequency of the agents’ indicative words, and the characterization’s ‘gain’/‘loss’, to avoid misleading words with low frequencies.
 
4
As we have already mentioned, there is no stochasticity during the dataset creation. However, by employing this repetition of the learning procedure per game, we ensure the robustness of our results.
 
5
A larger number of topics allows for more preferences sub-formulae to be learned, but it naturally increases complexity.
 
Literature
1.
Aziz, H., Harrenstein, P., Lang, J., Wooldridge, M.: Boolean hedonic games. In: 15th International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 166–175 (2016)
2.
Aziz, H., Savani, R., Moulin, H.: Hedonic Games. In: Handbook of Computational Social Choice, pp. 356–376. Cambridge University Press (2016)
3.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
4.
Bogomolnaia, A., Jackson, M.O.: The stability of hedonic coalition structures. Games Econ. Behav. 38(2), 201–230 (2002)MathSciNetCrossRef
5.
Bogomolnaia, A., Moulin, H., Stong, R.: Collective choice under dichotomous preferences. J. Econ. Theory 122(2), 165–184 (2005)MathSciNetCrossRef
6.
Brandt, F., Conitzer, V., Endriss, U., Lang, J., Procaccia, A.D.: Handbook of Computational Social Choice. Cambridge University Press, Cambridge (2016)CrossRef
7.
Chalkiadakis, G., Boutilier, C.: Bayesian reinforcement learning for coalition formation under uncertainty. In: Proceedings of the 3rd AAMAS, vol. 3. pp. 1090–1097. IEEE Computer Society, Washington (2004)
8.
Chalkiadakis, G., Elkind, E., Wooldridge, M.: Computational Aspects of Cooperative Game Theory (Synthesis Lectures on Artificial Inetlligence and Machine Learning), 1st edn. Morgan & Claypool Publishers, San Rafael (2011)
9.
10.
11.
Hoffman, M.D., Blei, D.M., Bach, F.: Online learning for latent dirichlet allocation. In: Proceedings of NIPS, USA, pp. 856–864 (2010)
12.
Jordan, M.I. (ed.): Learning in Graphical Models. MIT Press, Cambridge (1999)
13.
Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 183–233 (1999)CrossRef
14.
Kraus, S., Shehory, O., Taase, G.: The advantages of compromising in coalition formation with incomplete information. In: Proceedings of the 3rd AAMAS, pp. 588–595 (2004)
15.
16.
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
17.
Peters, D.: Complexity of hedonic games with dichotomous preferences. In: AAAI (2016)
18.
Sliwinski, J., Zick, Y.: Learning hedonic games. In: Proceedings of the 26th IJCAI-17, pp. 2730–2736 (2017)
19.
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101, 1566–1581 (2006)MathSciNetCrossRef
Metadata
Title
Learning Hedonic Games via Probabilistic Topic Modeling
Authors
Athina Georgara
Thalia Ntiniakou
Georgios Chalkiadakis
Copyright Year
2019
Book
Multi-Agent Systems

Print ISBN: 978-3-030-14173-8

Electronic ISBN: 978-3-030-14174-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-14174-5_5

Premium Partner

    Image Credits