Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing
This addresses the challenge of managing conflicting objectives in crowdsourcing marketplaces, offering a practical solution for task requesters, though it is incremental by extending prior two-objective optimizations to three.
The paper tackles the problem of jointly optimizing cost, quality, and time in micro-crowdsourcing by introducing Octopus, a hierarchical POMDP-based AI agent, which significantly outperforms existing state-of-the-art approaches in real experiments and real-world deployments.
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.