Optimal Complex Task Assignment in Service Crowdsourcing

Optimal Complex Task Assignment in Service Crowdsourcing

Feilong Tang

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1563-1569. https://doi.org/10.24963/ijcai.2020/217

Existing schemes cannot assign complex tasks to the most suitable workers because they either cannot measure skills quantitatively or do not consider assigning tasks to workers who are the most suitable but unavailable temporarily. In this paper, we investigate how to realize optimal complex task assignment. Firstly, we formulate the multiple-skill based task assignment problem in service crowdsourcing. We then propose a weighted multi-skill tree (WMST) to model multiple skills and their correlations. Next, we propose the acceptance expectation to uniformly measure the probabilities that different categories of workers will accept and complete specified tasks. Finally, we propose an acceptance-expectation-based task assignment (AE-TA) algorithm, which reserves tasks for the most suitable workers even unavailable temporarily. Comprehensive experimental results demonstrate that our WMST model and AE-TA algorithm significantly outperform related proposals.
Keywords:
Humans and AI: Human Computation and Crowdsourcing
Humans and AI: Human-AI Collaboration