Combining Spatial Optimization and Multi-Agent Temporal Difference Learning for Task Assignment in Uncertain Crowdsourcing

Abstract

In recent years, spatial crowdsourcing has emerged as an important new framework, in which each spatial task requires a set of right crowd-workers in the near vicinity to the target locations. Previous studies have focused on spatial task assignment in the static crowdsourcing environment. These algorithms may achieve local optimality by neglecting the uncertain features inherent in real-world crowdsourcing environments, where workers may join or leave during run time. Moreover, spatial task assignment is more complicated when large-scale crowd-workers exist in crowdsourcing environments. The large-scale nature of task assignments poses a significant challenge to uncertain spatial crowdsourcing. In this paper, we propose a novel algorithm combining spatial optimization and multi-agent temporal difference learning (SMATDL). The combination of grid-based optimization and multi-agent learning can achieve higher adaptability and maintain greater efficiency than traditional learning algorithms in the face of large-scale crowdsourcing problems. The SMATDL algorithm decomposes the uncertain crowdsourcing problem into numerous sub-problems by means of a grid-based optimization approach. In order to adapt to the change in the large-scale environment, each agent utilizes temporal difference learning to handle its own spatial region optimization in online crowdsourcing. As a result, multiple agents in SMATDL collaboratively learn to optimize their efforts in accomplishing the global assignment problems efficiently. Through extensive experiments, we illustrate the effectiveness and efficiency of our proposed algorithms on the experimental data sets.