Vio: a mixed-initiative approach to learning and automating procedural update tasks

Today many workers spend too much of their time translating their co-workers' requests into structures that information systems can understand. This paper presents the novel interaction design and evaluation of VIO, an agent that helps workers trans late request. VIO monitors requests and makes suggestions to speed up the translation. VIO allows users to quickly correct agent errors. These corrections are used to improve agent performance as it learns to automate work. Our evaluations demonstrate that this type of agent can significantly reduce task completion time, freeing workers from mundane tasks.

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