Crowdsourcing provides a new problem-solving paradigm that utilizes the intelligence of crowds to solve computer-hard problems. Task assignment is a foundation problem in crowdsourcing systems and applications. However, existing task assignment approaches often assume that workers operate independently. In reality, worker cooperation is necessary. In this paper, we address the cooperative task assignment (CTA) problem where a worker needs to pay a monetary cost to another worker in exchange for cooperation. Cooperative working also requires one task to be assigned to more than one worker to ensure the reliability of crowdsourcing services. We formalize the CTA problem with the goal of minimizing the total cooperation cost of all workers under the workload limitation of each worker. The challenge is that the individual cooperation cost that a worker pays for a specific task highly depends on the task distribution. This increases the difficulty of obtaining the assignment instance with a small cooperation cost. We prove that the CTA problem is NP-hard. We propose a two-stage cooperative task assignment framework that first assigns each task to one worker and then makes duplicate assignments. We also present solutions to address the dynamic scenarios. Extensive experimental results show that the proposed framework can effectively reduce the cooperation cost.

中文翻译：

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在众包系统中具有最低合作成本的协作任务分配框架


众包提供了一种新的问题解决范式，它利用群体的智能来解决计算机难题。任务分配是众包系统和应用程序中的一个基本问题。但是，现有的任务分配方法通常假设 worker 独立运行。实际上，工人的合作是必要的。在本文中，我们解决了合作任务分配 （CTA） 问题，其中一名工人需要向另一名工人支付货币成本以换取合作。协作工作还需要将一项任务分配给多个工作人员，以确保众包服务的可靠性。我们将 CTA 问题正式化，目标是在每个工人的工作量限制下最小化所有工人的总合作成本。挑战在于，工作人员为特定任务支付的单独合作成本在很大程度上取决于任务分配。这增加了以较小的合作成本获取 assignment 实例的难度。我们证明了 CTA 问题是 NP 困难的。我们提出了一个两阶段的协作任务分配框架，首先将每个任务分配给一个工作人员，然后进行重复分配。我们还提供了解决动态场景的解决方案。大量的实验结果表明，所提框架能够有效降低合作成本。