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Efficient Collaborative Crowdsourcing


Authors: Z. Pan, H. Yu, C. Miao, and C. Leung
Title: Efficient Collaborative Crowdsourcing
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.
Keywords: 
Conference Name: 30th AAAI Conference on Artificial Intelligence (AAAI'16)
Location: Phoenix, USA
Publisher: AAAI
Year: 2016
Accepted PDF File: Efficient_Collaborative_Crowdsourcing_accepted.pdf
Permanent Link: http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12106
Reference: Z. Pan, H. Yu, C. Miao, and C. Leung, “Efficient collaborative crowdsourcing,” in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI’16). AAAI, February 2016, pp. 4248–4249.
bibtex: 
@inproceedings{LILY-c69, 
    author	= {Pan, Zhengxiang and Yu, Han and Miao, Chunyan and Leung, Cyril},
    title	= {Efficient Collaborative Crowdsourcing},  
    booktitle	= {Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16)}, 
    year		= {2016}, 
    month	= {February}, 
    pages	= {4248-4249}, 
    location	= {Phoenix, USA},
    publisher	= {AAAI},
 }