Octopus：众包成本-质量-时间优化框架
Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing
摘要：
我们介绍了Octopus，一个人工智能智能体，它可以在微型众包市场上共同平衡三个相互冲突的任务目标-工作质量，总成本和完成时间。以前的控制代理主要集中在成本质量或成本时间权衡，但没有直接控制所有三个一致。一个简单的三目标优化公式是棘手的; Octopus采用分层POMDP方法，有三个不同的组件负责设置每个任务的报酬，选择下一个任务，并控制任务级质量。我们证明，章鱼显着优于现有的国家的最先进的方法在真实的实验。我们还在Amazon Mechanical Turk上部署了Octopus，展示了它在真实动态环境中管理任务的能力。
英文摘要：
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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