研究人员开发了一个新的成本感知多臂老虎机框架，以优化移动众包中的工人招募。该框架解决了工人表现不断变化的问题（个人会随着经验而提高），并考虑了未知、可变的成本。该模型联合学习工人的表现轨迹和成本，识别表现何时饱和，并分配有限的预算以最大化长期的感知效用。实验表明，与忽略这些动态或假设固定成本的方法相比，该方法取得了持续的改进。 AI
影响 该框架可以提高移动众包应用中大规模数据收集的效率和成本效益。
排序理由 该集群包含一篇在arXiv上发表的关于新算法框架的论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2607.13546v1 Announce Type: new Abstract: Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing. A fundamental challenge is online worker recrui…
Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing. A fundamental challenge is online worker recruitment under uncertainty, where the platform must…
Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing. A fundamental challenge is online worker recruitment under uncertainty, where the platform must…