Robust Online Crowdsourcing with Strategic Workers

Crowdsourcing has facilitated a wide range of applications by leveraging public workers to contribute large number of tasks. However, most prior works only considered static environments and overlooked the system dynamics. In practice, the task set to be allocated is time-varying and the workers may be strategic when deciding whether to accept the tasks. In this paper, we formulate the online crowdsourcing problem as a sequential optimization problem, where a requestor needs to allocate tasks repeatedly to the workers to maximize the long-term cumulative utility. To deal with the dynamics, we first build an environmental model to predict the system dynamics. The model can also embed the tasks into a fixed lower-dimensional space. Next, we propose a multi-agent reinforcement learning algorithm to optimize the allocation mechanism for the requestor. The underlying intuition is that the mechanism can be robust even with adversarial workers. In the experiment, we conducted extensive experiments to evaluate the performance. The results validate that our method can achieve the best performance in almost all cases. The results are robust when deployed in an adversarial environment.