基于纳什竞价的空间众包任务定价算法

Spatial Crowdsourcing Task Pricing Algorithm Based on Nash Bidding

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。

Task pricing is an important step for crowdsourcing platforms to solve profit-driven task allocation and maximize profits.However,there are relatively few studies on task pricing about worker expectations,and most existing studies do not consider the dynamic demands of workers and tasks.Furthermore,obtaining complete worker information is difficult due to worker privacy and sensor limitations.In order to solve the above problems,a pricing algorithm for spatial crowdsourcing tasks based on nash bidding is proposed.The algorithm first obtains the price range of the task through the machine learning algorithm,and then conducts nash bidding on the price range.In order to solve the problem of large price fluctuations caused by dynamic supply and demand,an adjustment mechanism is designed to stabilize the average price of tasks.Finally,in order to simulate the Nash equilibrium point,two different gradient functions are used to search for the task price with the largest number of matches.The proposed algorithm is tested on the gMission data set and the synthetic data set respectively.The results show that the algorithm is 60%and 1.57 times of the MCMF algorithm in terms of the number of matches and the average task price,and the time cost is 9.6%of the MCMF algorithm.Experimental results show the effectiveness of the proposed algorithm.