基于多Agent的众包任务推荐系统建模与仿真

为了让众包平台用户更方便准确地搜寻到合适任务,促进其能力水平提升,解决众包任务推荐动态性等问题,提出了一种基于多Agent的众包任务推荐系统。首先,基于众包平台建立多Agent任务推荐模型,提出了模型设计思路与模型框架,并进一步阐述了各Agent功能、相互作用关系与相关算法;其次,提出众包用户能力水平提升相关算法;最后,利用NetLogo仿真软件进行验证。结果表明,众包任务推荐系统可对用户能力水平的提升起到促进作用,证明了在众包平台引入推荐系统的必要性。并且分析了多Agent技术可提升推荐系统的动态性、智能性与灵活性等整体性能,促进了众包平台数据的管理与维护。

Multi-agent based modeling and simulation ofcrowdsourcing task recommendation system

In order to help crowdsourcing platform users search for right tasks conveniently and accurately, promote their ability and solve the dynamic problem of crowdsourcing task recommendation, we propose a crowdsourcing task recommendation system based on multi-agent. Firstly, we build the multi-agent task recommendation system model based on the crowdsourcing platform, and describe the design thought and framework of the model. In addition, we expound the function of each agent, the interaction relations between agents and the related algorithms. Secondly, we propose some related algorithms to promote crowdsourcing users’ ability. Finally, we use the simulation software NetLogo to verify the model. The results show that the proposed crowdsourcing task recommendation system can make contribution to the promotion of users’ ability, which proves the necessity of task recommendation systems on crowdsourcing platforms. Our analysis shows that the multi-agent technique can improve the overall performance of the task recommendation system, such as dynamism, intellectuality and flexibility. It can also promote the management and maintenance of crowdsourcing platform data.