Quantum Analysis on Task Allocation and Quality Control for Crowdsourcing With Homogeneous Workers

Quantum Analysis on Task Allocation and Quality Control for Crowdsourcing With Homogeneous Workers
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DOI:
10.1109/tnse.2020.2997716
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发表时间:
2020-10
影响因子:
6.6
通讯作者:
Minghui Xu;Shengling Wang;Qin Hu;Hao Sheng;Xiuzhen Cheng
Minghui Xu;Shengling Wang;Qin Hu;Hao Sheng;Xiuzhen Cheng
中科院分区:
计算机科学3区
文献类型:
--
作者:
Minghui Xu;Shengling Wang;Qin Hu;Hao Sheng;Xiuzhen Cheng

文献摘要

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Crowdsourcing has been emerging as a valid problem-solving model that harnesses a large group of contributors to solve a complicated task. However, existing crowdsourcing platforms or systems could suffer from task allocation and quality control problems. In this article, we first prove that there exist two dilemmas while tackling the above issues by using a game-theoretic approach. To overcome this challenge, we are focusing on exploiting quantum crowdsourcing schemes in which the welfare of requestor or worker can be maximized since quantum players share the extended strategy space, and the introduction of entanglement offers a new method of depicting fine-grained relations between players. Specifically, we propose a quantum game model for quota-oriented crowdsourcing game to address dilemmas in task allocation. The result indicates the dilemma based on classical strategy will disappear with the increment of entanglement degree. While in the quality-oriented crowdsourcing game, we adopt a density matrix approach to calculate the optimal payoffs of both sides, which demonstrates the superiority of our quantum strategy. Moreover, our quantum scheme is generic since it is compatible with the schemes from a classical perspective. Hence, our noteworthy quantum crowdsourcing schemes offer a promising alternative route for tackling dilemmas in crowdsourcing scenarios.