RADAR is a multiagent system with a mixed-initiative user interface designed to help office workers cope with email overload. RADAR agents observe experts to learn models of their strategies and then use the models to assist other people who are working on similar tasks. The agents' assistance helps a person to transition from the normal emailcentric workflow to a more efficient task-centric workflow. The Email Classifier learns to identify tasks contained within emails and then inspects new emails for similar tasks. A novel task-management user interface displays the found tasks in a to-do list, which has integrated support for performing the tasks. The Multitask Coordination Assistant learns a model of the order in which experts perform tasks and then suggests a schedule to other people who are working on similar tasks. A novel Progress Bar displays the suggested schedule of incomplete tasks as well as the completed tasks. A large evaluation demonstrated that novice users confronted with an email overload test performed significantly better (a 37% better overall score with a factor of four fewer errors) when assisted by the RADAR agents.

We developed a mixed-initiative e-mail system, modeled after a personalized assistant, which uses AI learning techniques to reduce e-mail overload. RADAR (reflective agents with distributed adaptive reasoning) was a five-year interdisciplinary proj-

A key challenge for mixed-initiative systems is to create a shared understanding of the task between human and agent. To address this challenge, we created a mixed-initiative interface called Mixer to aid administrators with automating tedious information-retrieval tasks. Users initiate communication with the agent by constructing a form, creating a structure to hold the information they require and to show context in order to interpret this information. They then populate the form with the desired results, demonstrating to the agent the steps required to retrieve the information. This method of form creation explicitly defines the shared understanding between human and agent. An evaluation of the interface shows that administrators can effectively create forms to communicate with the agent, that they are likely to accept this technology in their work environment, and that the agent's help can significantly reduce the time they spend on repeated informationretrieval 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 translate 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.

No abstract