A quality assuring multi-armed bandit crowdsourcing mechanism with incentive compatible learning
نویسندگان
چکیده
We develop a novel multi-armed bandit (MAB) mechanism for the problem of selecting a subset of crowd workers to achieve an assured accuracy for each binary labelling task in a cost optimal way. This problem is challenging because workers have unknown qualities and strategic costs.
منابع مشابه
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
Consider a requester who wishes to crowdsource a series of identical binary labeling tasks from a pool of workers so as to achieve an assured accuracy for each task, in a cost optimal way. The workers are heterogeneous with unknown but fixed qualities and moreover their costs are private. The problem is to select an optimal subset of the workers to work on each task so that the outcome obtained...
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تاریخ انتشار 2014