Efficient Task Sub-Delegation for Crowdsourcing

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i. e. , workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker’s reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.

Authors

• Han Yu Nanyang Technological University, Singapore
• Chunyan Miao Nanyang Technological University
• Zhiqi Shen Nanyang Technological University
• Cyril Leung
• Yiqiang Chen Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences Peng Cheng Laboratory Beijing Key Laboratory of Mobile Computing and Pervasive Device
• Qiang Yang Hong Kong University of Science and Technology, Hong Kong SAR, China

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