JIANG Huan, ZUO Min, MATSUBARA Shigeo. Efficient Task Decomposition for Sequential Crowdsourced Task Solving[J]. Chinese Journal of Electronics, 2020, 29(3): 468-475. doi: 10.1049/cje.2020.03.003
Citation: JIANG Huan, ZUO Min, MATSUBARA Shigeo. Efficient Task Decomposition for Sequential Crowdsourced Task Solving[J]. Chinese Journal of Electronics, 2020, 29(3): 468-475. doi: 10.1049/cje.2020.03.003

Efficient Task Decomposition for Sequential Crowdsourced Task Solving

doi: 10.1049/cje.2020.03.003
Funds:  This work is supported by the National Key Research and Development Project (No.2016YFD0401205), Humanity and Social Science Youth Foundation of Ministry of Education of China (No.17YJCZH007), Beijing Natural Science Foundation (No.4202014) and JSPS KAKENHI (No.JP17H00759 and No.JP19H04170).
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  • Corresponding author: ZUO Min (corresponding author) was born in 1973. He received the Ph.D. degree in computer application from University of Science and Technology Beijing. He is now a professor and a master supervisor at Beijing Technology and Business University. His research interests include intelligent management and artificial intelligence. (Email:zuomin1234@163.com)
  • Received Date: 2018-08-04
  • Rev Recd Date: 2019-11-18
  • Publish Date: 2020-05-10
  • In order to facilitate crowdsourcing-based task solving, complex tasks are decomposed into smaller subtasks that can be executed by individual workers. Decomposing task into sequential subtasks attracts a plenty of empirical explorations. The absence of formal studies makes it difficult to provide task requesters with explicit guidelines on task decomposition strategy. We formally present the vertical task decomposition model by specifying the positive quality dependencies among sequential subtasks. Our focus is on addressing solutions of low quality intentionally provided by selfinterested workers who are paid equally or based on their contributions. By combining the theoretical analysis on workers’ strategic behaviors and experimental exploration on the efficiency of task decomposition, our study demonstrates the relationship between the incentive and the worker’s performance, and gives the explicit instructions on vertical task decomposition, which show promise on improving the quality of the final outcome.
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