Efficient Task Sub-Delegation for Crowdsourcing


  • Han Yu Nanyang Technological University
  • Chunyan Miao Nanyang Technological University
  • Zhiqi Shen Nanyang Technological University
  • Cyril Leung The University of British Columbia
  • Yiqiang Chen Chinese Academy of Sciences
  • Qiang Yang Hong Kong University of Science and Technology




Crowdsourcing, Human Computation, Reputation, Trust Networks, Task Sub-delegation


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.




How to Cite

Yu, H., Miao, C., Shen, Z., Leung, C., Chen, Y., & Yang, Q. (2015). Efficient Task Sub-Delegation for Crowdsourcing. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9337



AAAI Technical Track: Human-Computation and Crowd Sourcing