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  Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., & Schölkopf, B. (2019). Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. In 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019) (pp. 286-295). Red Hook, NY, USA: Curran.

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Genre: Konferenzbeitrag

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http://auai.org/uai2019/proceedings/papers/49.pdf (Verlagsversion)
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 Urheber:
Geiger, P, Autor
Besserve, M1, 2, Autor           
Winkelmann, J, Autor
Proissl, C, Autor
Schölkopf, B3, Autor           
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Zusammenfassung: We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which assistant algorithms reach optimal predictions?
First we lay conceptual ground for this setting where user preferences are a priori unknown and predictions influence outcomes. Addressing (1), we establish conditions under which self-fulfilling prophecies, i.e., "perfect" (probabilistic) predictions of what will happen, solve the coordination problem in the game-theoretic sense of selecting a Bayesian Nash equilibrium (BNE). Next we prove that such prophecies exist even in large-scale settings where only aggregated statistics about users are available. This entails a new (nonatomic) BNE existence result. Addressing (2), we propose two assistant algorithms that sequentially learn from users' reactions, together with optimality/convergence guarantees. We validate one of them in a large real-world experiment.

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 Datum: 2019-072019
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
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Veranstaltung

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Titel: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019)
Veranstaltungsort: Tel Aviv, Israel
Start-/Enddatum: 2019-07-22 - 2019-07-25

Entscheidung

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Projektinformation

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Quelle 1

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Titel: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019)
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: 49 Start- / Endseite: 286 - 295 Identifikator: ISBN: 978-1-5108-9156-2

Quelle 2

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Titel: Proceedings of Machine Learning Research
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 115 Artikelnummer: - Start- / Endseite: 207 - 216 Identifikator: ISSN: 2640-3498