Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2015

Abstract

In this work, we investigate the problem of large-scale 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.

Keywords

Deterministic modeling, Industry practices, Integer linear program, sLagrangian relaxation techniques, Location based, Mobile crowdsourcing, Stochastic task, Transportation network

Discipline

Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015): Buenos Aires, Argentina, 25–31 July 2015

First Page

1113

Last Page

1119

ISBN

9781577357384

Publisher

AAAI Press

City or Country

Palo Alto, CA

Copyright Owner and License

Authors

Additional URL

https://ijcai.org/papers15/Papers/IJCAI15-161.pdf

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