Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing

2017-10-27

Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 5

Volume

Vol. 5 (2017): Fifth AAAI Conference on Human Computation and Crowdsourcing

Full Papers

We present Octopus, an AI agent to jointly balance three conflicting task objectives on a micro-crowdsourcing marketplace – the quality of work, total cost incurred, and time to completion. Previous control agents have mostly focused on cost-quality, or cost-time tradeoffs, but not on directly controlling all three in concert. A naive formulation of three-objective optimization is intractable; Octopus takes a hierarchical POMDP approach, with three different components responsible for setting the pay per task, selecting the next task, and controlling task-level quality. We demonstrate that Octopus significantly outperforms existing state-of-the-art approaches on real experiments. We also deploy Octopus on Amazon Mechanical Turk, showing its ability to manage tasks in a real-world, dynamic setting.

10.1609/hcomp.v5i1.13311