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

Karan Goel; Shreya Rajpal; Mausam

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arXiv · 1702.03488

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

Karan Goel·Shreya Rajpal·Mausam

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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 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.

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BibTeX RIS

Karan Goel, Shreya Rajpal, Mausam. 2017-02-12. Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing. https://arxiv.org/abs/1702.03488

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