Articles liés à Game Theory for Data Science: Eliciting Truthful Information
Synopsis
Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards.
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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À propos de l?auteur
Boi Faltings is a full professor at École Polytechnique Fédérale 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 multiagent 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.
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Game Theory for Data Science: Eliciting Truthful Information (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Morgan & Claypool Publishers, 2017
ISBN 10 : 1627057293
ISBN 13 : 9781627057295
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Game Theory for Data Science: Eliciting Truthful Information (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Edité par
Morgan & Claypool Publishers, 2017
ISBN 10 : 1627057293
ISBN 13 : 9781627057295
Ancien ou d'occasion
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Game Theory for Data Science: Eliciting Truthful Information (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Edité par
Morgan & Claypool Publishers, 2017
ISBN 10 : 1627057293
ISBN 13 : 9781627057295
Ancien ou d'occasion
Couverture souple
Vendeur : Scissortail, Oklahoma City, OK, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition.Please note:-May include library or rental stickers, stamps, or markings.-Supplemental materials e.g., CDs, access codes, inserts are not guaranteed.-Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. -Sourced from donation centers; authenticity not verified with publisher. Your satisfaction is our top priority! If you have any questions or concerns about your order, please donât hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading! N° de réf. du vendeur STM.9A9
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Game Theory for Data Science: Eliciting Truthful Information
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Game Theory for Data Science: Eliciting Truthful Information (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Edité par
Morgan & Claypool Publishers, 2017
ISBN 10 : 1627057293
ISBN 13 : 9781627057295
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