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Efficient Task Sub-delegation for Crowdsourcing

Authors: H. Yu, C. Miao, Z. Shen, C. Leung, Y. Chen, and Q. Yang
Title: Efficient Task Sub-delegation for Crowdsourcing
Abstract: 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.
Conference Name: 29th AAAI Conference on Artificial Intelligence (AAAI’15)
Location: Austin, USA
Publisher: AAAI
Year: 2015
Accepted PDF File: Efficient_Task_Sub-delegation_for_Crowdsourcing_accepted.pdf
Permanent Link:
Reference: H. Yu, C. Miao, Z. Shen, C. Leung, Y. Chen, and Q. Yang, “Efficient task sub-delegation for crowdsourcing,” in Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15). AAAI, January 2015, pp. 1305–1311.
   author	= {Yu, Han and Miao, Chunyan and Shen, Zhiqi and Leung, Cyril and Chen, Yiqiang and Yang, Qiang}, 
   title	= {Efficient Task Sub-delegation for Crowdsourcing}, 
   booktitle	= {Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15)}, 
   year		= {2015}, 
   month	= {January}, 
   pages	= {1305-1311}, 
   location	= {Austin, USA},
   publisher	= {AAAI},