Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies

Weici Hu, Peter Frazier
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:324-332, 2016.

Abstract

We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Markov decision process, but the curse of dimensionality renders the computation infeasible. Following a similar approach to the Lagrangian Relaxation technique in Adelman and Mersereau (2008), we provide a computationally tractable instance-specific upper bound on the value of this Bayes-optimal policy, which can in turn be used to bound the optimality gap of any other sub-optimal policy. In an approach similar in spirit to the Whittle index for restless multi-armed bandits, we provide an index policy for effort allocation in crowdsourcing and demonstrate numerically that it outperforms other state-of-the-art policies and performs close to optimal.

Cite this Paper

BibTeX
@InProceedings{pmlr-v51-hu16a, title = {Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies}, author = {Hu, Weici and Frazier, Peter}, booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics}, pages = {324--332}, year = {2016}, editor = {Gretton, Arthur and Robert, Christian C.}, volume = {51}, series = {Proceedings of Machine Learning Research}, address = {Cadiz, Spain}, month = {09--11 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v51/hu16a.pdf}, url = {https://proceedings.mlr.press/v51/hu16a.html}, abstract = {We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Markov decision process, but the curse of dimensionality renders the computation infeasible. Following a similar approach to the Lagrangian Relaxation technique in Adelman and Mersereau (2008), we provide a computationally tractable instance-specific upper bound on the value of this Bayes-optimal policy, which can in turn be used to bound the optimality gap of any other sub-optimal policy. In an approach similar in spirit to the Whittle index for restless multi-armed bandits, we provide an index policy for effort allocation in crowdsourcing and demonstrate numerically that it outperforms other state-of-the-art policies and performs close to optimal.} }
Endnote
%0 Conference Paper %T Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies %A Weici Hu %A Peter Frazier %B Proceedings of the 19th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2016 %E Arthur Gretton %E Christian C. Robert %F pmlr-v51-hu16a %I PMLR %P 324--332 %U https://proceedings.mlr.press/v51/hu16a.html %V 51 %X We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Markov decision process, but the curse of dimensionality renders the computation infeasible. Following a similar approach to the Lagrangian Relaxation technique in Adelman and Mersereau (2008), we provide a computationally tractable instance-specific upper bound on the value of this Bayes-optimal policy, which can in turn be used to bound the optimality gap of any other sub-optimal policy. In an approach similar in spirit to the Whittle index for restless multi-armed bandits, we provide an index policy for effort allocation in crowdsourcing and demonstrate numerically that it outperforms other state-of-the-art policies and performs close to optimal.
RIS
TY - CPAPER TI - Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies AU - Weici Hu AU - Peter Frazier BT - Proceedings of the 19th International Conference on Artificial Intelligence and Statistics DA - 2016/05/02 ED - Arthur Gretton ED - Christian C. Robert ID - pmlr-v51-hu16a PB - PMLR DP - Proceedings of Machine Learning Research VL - 51 SP - 324 EP - 332 L1 - http://proceedings.mlr.press/v51/hu16a.pdf UR - https://proceedings.mlr.press/v51/hu16a.html AB - We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Markov decision process, but the curse of dimensionality renders the computation infeasible. Following a similar approach to the Lagrangian Relaxation technique in Adelman and Mersereau (2008), we provide a computationally tractable instance-specific upper bound on the value of this Bayes-optimal policy, which can in turn be used to bound the optimality gap of any other sub-optimal policy. In an approach similar in spirit to the Whittle index for restless multi-armed bandits, we provide an index policy for effort allocation in crowdsourcing and demonstrate numerically that it outperforms other state-of-the-art policies and performs close to optimal. ER -
APA
Hu, W. & Frazier, P.. (2016). Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 51:324-332 Available from https://proceedings.mlr.press/v51/hu16a.html.

Related Material