Conference Proceeding Article
In this work, we investigate the problem of largescale mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually pick tasks to perform, we automatically make task recommendation based on workers’ historical trajectories and desired time budgets. The challenge of predicting workers’ trajectories is that it is faced with uncertainties, as a worker does not take same routes every day. In this work, we depart from deterministic modeling and study the stochastic task recommendation problem where each worker is associated with several predicted routine routes with probabilities. We formulate this problem as a stochastic integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures of the formulation and apply the Lagrangian relaxation technique to scale up computation. Experiments have been performed over the instances generated using the real Singapore transportation network. The results show that we can find significantly better solutions than the deterministic formulation.
Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics; Software and Cyber-Physical Systems
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015): Buenos Aires, Argentina, 25–31 July 2015
City or Country
Palo Alto, CA
CHEN CEN; CHENG, Shih-Fen; LAU, Hoong Chuin; and MISRA, Archan.
Towards City-scale Mobile Crowdsourcing: Task Recommendations under Trajectory Uncertainties. (2015). Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015): Buenos Aires, Argentina, 25–31 July 2015. 1113-1119. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2815