Game Theory for Data Science

Eliciting Truthful Information

Häftad, Engelska, 2017

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Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation.

Produktinformation

Utgivningsdatum2017-09-19
Mått191 x 235 x 9 mm
Vikt298 g
FormatHäftad
SpråkEngelska
SerieSynthesis Lectures on Artificial Intelligence and Machine Learning
Antal sidor135
FörlagSpringer International Publishing AG
ISBN9783031004490
OriginaltitelGame Theory for Data Science

Tillhör följande kategorier

Tillämpad matematik inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it

Boi Faltings is a full professor at Ecole Polytechnique Federale de Lausanne (EPFL) and has worked in AI since 1983. He is one of the pioneers on the topic of mechanisms for truthful information elicitation, with the first work dating back to 2003. He has taught AI and multi-agent systems to students at EPFL for 28 years. He is a fellow of AAAI and ECCAI and has served on program committee and editorial boards of the major conferences and journals in Artificial Intelligence.Goran Radanovic has been a post-doctoral fellow at Harvard University since 2016. He received his Ph.D. from the Swiss Federal Institute of Technology and has worked on the topic of mechanisms for information elicitation since 2011. His work has been published mainly at AI conferences.

Preface.- Acknowledgments.- Introduction.- Mechanisms for Verifiable Information.- Parametric Mechanisms for Unverifiable Information.- Nonparametric Mechanisms: Multiple Reports.- Nonparametric Mechanisms: Multiple Tasks.- Prediction Markets: Combining Elicitation and Aggregation.- Agents Motivated by Influence.- Decentralized Machine Learning.- Conclusions.- Bibliography.- Authors' Biographies .