Incentivizing the Workers for Truth Discovery in Crowdsourcing with Copiers
Incentivizing the Workers for Truth Discovery in Crowdsourcing with Copiers
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DOI：
10.1109/icdcs.2019.00129
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Lingyun Jiang;Xiaofu Niu;Jia Xu;Dejun Yang;Lijie Xu
中科院分区：
文献类型：
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作者：
Lingyun Jiang;Xiaofu Niu;Jia Xu;Dejun Yang;Lijie Xu
众包已经成为执行大规模任务的有效范例。真相发现和激励机制对于众包系统至关重要。人们提出了许多众包的真相发现方法和激励机制。然而，其中大部分都不能应用于处理带有复印机的众包。为了解决这个问题，我们提出了社会福利最大化的问题，使得所有的任务都能以最小的信心来完成。我们设计了一个由真相发现阶段和逆向拍卖阶段组成的激励机制。在真值发现阶段，我们根据工作者的依赖性和准确性来估计每个任务的真值。在逆向拍卖阶段，我们设计了一种贪婪算法来选择中标者并确定支付金额。通过严格的理论分析和广泛的仿真，我们证明了所提出的机制具有计算效率、个体合理性、真实性和保证逼近性。此外，当众包系统中存在模仿者时，我们的真相发现方法在准确性方面具有突出的优势。
Crowdsourcing has become an efficient paradigm for performing large scale tasks. Truth discovery and incentive mechanism are fundamentally important for the crowdsourcing system. Many truth discovery methods and incentive mechanisms for crowdsourcing have been proposed. However, most of them cannot be applied to dealing with the crowdsourcing with copiers. To address the issue, we formulate the problem of maximizing the social welfare such that all tasks can be completed with the least confidence for truth discovery. We design an incentive mechanism consisting of truth discovery stage and reverse auction stage. In truth discovery stage, we estimate the truth for each task based on both the dependence and accuracy of workers. In reverse auction stage, we design a greedy algorithm to select the winners and determine the payment. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation. Moreover, our truth discovery method shows prominent advantage in terms of precision when there are copiers in the crowdsourcing systems.