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

Abstract

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 threeobjective 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.