计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 184-192.doi: 10.11896/jsjkx.220900130
林韦达, 董红斌, 赵炳旭
LIN Weida, DONG Hongbin, ZHAO Bingxu
摘要： 任务定价是众包平台解决利润驱动的任务分配、最大化利润的重要步骤。然而关于工人期望的任务定价研究相对较少,现有大多数研究并不考虑工人与任务的动态需求。此外,出于工人隐私和传感器限制,获取完整的工人信息是困难的。为解决上述难题,提出了基于纳什竞价的空间众包任务定价算法。首先通过机器学习算法获取任务的价格范围,然后在价格区间上进行纳什竞价。为了解决动态供需造成的价格大幅波动问题,设计调整机制来稳定任务均价。最后为模拟纳什均衡点,采用了两种不同的梯度递减函数,来搜索匹配数最大的任务定价。分别在gMission数据集和合成数据集进行了实验,结果表明所提算法的匹配数量和任务均价分别是MCMF算法的60%和1.57倍,时间花费是MCMF算法的9.6%,验证了所提算法的有效性。
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