Skill-and-Stress-Aware Assignment of Crowd-Worker Groups to Task Streams


  • Katsumi Kumai University of Tsukuba
  • Masaki Matsubara University of Tsukuba
  • Yuhki Shiraishi Tsukuba University of Technology
  • Daisuke Wakatsuki Tsukuba University of Technology
  • Jianwei Zhang Iwate University
  • Takeaki Shionome Teikyo University
  • Hiroyuki Kitagawa University of Tsukuba
  • Atsuyuki Morishima University of Tsukuba



Task assignments, Task-stream crowdsourcing, Worker-group queue


Worker-task assignments represent one of the critical issues in crowdsourcing, as they affect the quality of task results. This study addresses the problem of forming worker groups assigned to the same task in a task stream that requires more than one worker. We introduce a worker-group queue model that covers practical and common scenarios for task-stream crowdsourcing, and compare three strategies in terms of the skill balance among worker groups, the quality of the final outputs, the number of worker re-assignments of workers, and psychological stress felt by workers. We found that one of the compared strategies that employs multiple worker queues yields good results based on these measures.




How to Cite

Kumai, K., Matsubara, M., Shiraishi, Y., Wakatsuki, D., Zhang, J., Shionome, T., Kitagawa, H., & Morishima, A. (2018). Skill-and-Stress-Aware Assignment of Crowd-Worker Groups to Task Streams. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 6(1), 88-97.