Efficient Collaborative Crowdsourcing

Zhengxiang Pan, Han Yu, Chunyan Miao, Cyril Leung

AAAI 2016 pp. 4248-4249

doi:10.1609/AAAI.V30I1.9941 /aaai/2016/pan2016aaai-efficient/

Abstract

We consider the problem of making efficient quality-time-cost trade-offs in collaborative crowdsourcing systems in which different skills from multiple workers need to be combined to complete a task. We propose CrowdAsm - an approach which helps collaborative crowdsourcing systems determine how to combine the expertise of available workers to maximize the expected quality of results while minimizing the expected delays. Analysis proves that CrowdAsm can achieve close to optimal profit for workers in a given crowdsourcing system if they follow the recommendations.

PDF AAAI Semantic Scholar

Cite

Text

Pan et al. "Efficient Collaborative Crowdsourcing." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9941

Markdown

[Pan et al. "Efficient Collaborative Crowdsourcing." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/pan2016aaai-efficient/) doi:10.1609/AAAI.V30I1.9941

BibTeX

@inproceedings{pan2016aaai-efficient,
  title     = {{Efficient Collaborative Crowdsourcing}},
  author    = {Pan, Zhengxiang and Yu, Han and Miao, Chunyan and Leung, Cyril},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {4248-4249},
  doi       = {10.1609/AAAI.V30I1.9941},
  url       = {https://mlanthology.org/aaai/2016/pan2016aaai-efficient/}
}