AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence

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Efficient Task Sub-Delegation for Crowdsourcing
Han Yu, Chunyan Miao, Zhiqi Shen, Cyril Leung, Yiqiang Chen, Qiang Yang

Last modified: 2015-02-16


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.


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

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