A cooperative task assignment framework with minimum cooperation cost in crowdsourcing systems

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.