Game Theory for Data Science: Eliciting Truthful Information: Synthesis Lectures on Artificial Intelligence and Machine Learning
Autor Boi Faltings, Goran Radanovicen Limba Engleză Paperback – 19 sep 2017
We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.
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Specificații
ISBN-13: 9783031004490
ISBN-10: 3031004493
Ilustrații: XV, 135 p.
Dimensiuni: 191 x 235 mm
Greutate: 0.27 kg
Editura: Springer International Publishing
Colecția Springer
Seria Synthesis Lectures on Artificial Intelligence and Machine Learning
Locul publicării:Cham, Switzerland
ISBN-10: 3031004493
Ilustrații: XV, 135 p.
Dimensiuni: 191 x 235 mm
Greutate: 0.27 kg
Editura: Springer International Publishing
Colecția Springer
Seria Synthesis Lectures on Artificial Intelligence and Machine Learning
Locul publicării:Cham, Switzerland
Cuprins
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 .
Notă biografică
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.