Efficient crowdsourcing of unknown experts using bounded multi-armed bandits

DOI10.1016/j.artint.2014.04.005zbMath1405.68309OpenAlexW2150638405MaRDI QIDQ2014933

Long Tran-Thanh, Nicholas R. Jennings, Alex Rogers, Sebastian Stein

Publication date: 16 June 2014

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2014.04.005

zbMATH Keywords

machine learningcrowdsourcingmulti-armed banditsbudget limitation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Probabilistic games; gambling (91A60) Theory of organizations, manpower planning in operations research (90B70)

Related Items (4)

A quality assuring, cost optimal multi-armed bandit mechanism for expertsourcing ⋮ Truthful learning mechanisms for multi-slot sponsored search auctions with externalities ⋮ An optimal bidimensional multi-armed bandit auction for multi-unit procurement ⋮ Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback

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• Multi-armed Bandits with Metric Switching Costs
• Probability Inequalities for Sums of Bounded Random Variables
• Some aspects of the sequential design of experiments
• Finite-time analysis of the multiarmed bandit problem

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