Adaptive Task Assignment in Spatial Crowdsourcing: A Human-in-The-Loop Approach

In recent years, adaptive task assignment has been explored in spatial crowdsourcing. The challenge lies in how to adaptively partition the task stream to achieve the best utility for task assignment. A number of existing works have attempted to solve this challenge and achieve better performance by utilizing learning-based methods. Specifically, they mainly employ reinforcement learning to divide the task stream into a series of suitable batches and then perform task assignment in a batch fashion. Drawing inspiration from the effectiveness of human-machine collaborative decision-making, we aim to investigate human-in-the-loop methods to further enhance the performance of adaptive task assignment. In this paper, we propose a novel framework called Human-in-the-Loop Adaptive Partition (HLAP), which consists of two primary modules: Reinforcement Learning Partition Decision (RL-PD) and Human Supervision and Guidance (HSG). In the RL-PD module, we develop an RL agent, referred to as the decision-maker, by integrating the dual attention network into the Deep Q-Network (DQN) algorithm to capture cross-dimensional contextual information and long-range dependencies for a better understanding of the environment. In the HSG module, we design a human-in-the-loop mechanism to optimize the performance of the decision-maker, focusing on addressing two key issues: when and how humans interact with the decision-maker. Furthermore, to alleviate the heavy workload on humans, we construct a supervisor based on RL to oversee the decision-maker's partition process and adaptively determine when human intervention is necessary. We conduct extensive experiments on two real-world datasets, and the results demonstrate the efficiency and effectiveness of the HLAP framework.