Publication Type

Conference Proceeding Article

Publication Date



In this work, we investigate the problem of mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually browse and filter tasks to perform, we intend to automatically make task recommendations based on workers' historical trajectories and desired time budgets. However, predicting workers' trajectories is inevitably faced with uncertainties, as no one will take exactly the same route every day; yet such uncertainties are oftentimes abstracted away in the known literature. In this work, we depart from the 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 structure of the formulation and apply the Lagrangian relaxation technique to scale up the solution approach. 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.


crowdsourcing, mobile crowdsourcing, multiagent planning


Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering | Software Engineering

Research Areas

Intelligent Systems and Decision Analytics; Software and Cyber-Physical Systems


AAMAS '15: Proceedings of the 14th International Conference on Autonomous Agents and Multi-Agent Systems: 4-8 May 2015, Istanbul, Turkey

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City or Country

Richland, SC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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