Decision-Theoretic Control for Crowdsourced Workflows
Crowd-sourcing is a recent framework in which human intelligence tasks are outsourced to a crowd of unknown people (”workers”) as an open call (e.g., on Amazon’s Mechanical Turk). Crowd-sourcing has become immensely popular with hoards of employers (”requesters”), who use it to solve a wide variety of jobs, such as dictation transcription, content screening, etc. In order to achieve quality results, requesters often subdivide a large task into a chain of bite-sized sub-tasks that are combined into a complex, iterative workflow in which workers check and improve each other’s results. This project
raises an exciting question for AI — could an autonomous agent control these workflows without human intervention, yielding better results than today’s state of the art, a fixed control program?
Publications
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Artificial Intelligence for Artificial Artificial Intelligence Peng Dai, Mausam and Daniel S. Weld AAAI Conference on Artificial Intelligence, 2011. Full Paper (PDF) |
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Artificial Intelligence for Artificial Artificial Intelligence Peng Dai, Mausam and Daniel S. Weld Human Computation Workshop, 2011. Full Paper (PDF) |
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Human Intelligence Needs Artificial Intelligence Daniel S. Weld, Mausam and Peng Dai Human Computation Workshop, 2011. Full Paper (PDF) |
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Decision-Theoretic Control for Crowdsourced Workflows Peng Dai, Mausam and Daniel S. Weld AAAI Conference on Artificial Intelligence, 2010. Full Paper (PDF) |