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 email-centric 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.

The RADAR project developed a large multiagent system with a mixed‐initiative user interface designed to help office workers cope with email overload. Most RADAR agents observe experts performing tasks and then assist other users who are performing similar tasks. The interaction design for RADAR focused on developing user interfaces that allowed the intelligent functionality to improve the user's workflow without frustrating the user when the system's suggestions were either unhelpful or simply incorrect. For example, with regard to autonomy, the RADAR agents were allowed much flexibility in selecting ways to assist the user but were restricted from taking actions that would be visible to other people. This policy ensured that the user remained in control and mitigated the negative effects of mistakes. A large evaluation of RADAR demonstrated that novice users confronted with an e‐mail overload test performed significantly better, achieving a 37 percent better overall score when assisted by RADAR. The evaluation showed that AI technologies can help users accomplish their goals.

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 information-retrieval tasks.

Email clients were not designed to serve as a task management tools, but a high volume of task-relevant information in email leads many people to use email clients for this purpose. Such usage aggravates a user's experience of email overload and reduces productivity. Prior research systems have sought to address this problem by experimentally adding task management capabilities to email client software. RADAR (Reflective Agents with Distributed Adaptive Reasoning) takes a different approach in which a software agent acts like a trusted human assistant. Many RADAR components employ machine learning to improve their performance. Human participant studies showed a clear impact of learning on useI peIformance metrics.

We are designing, implemen ting, and testing the user interface for RADAR (Reflective Agents with Distributed Adaptive Reasoning), which is a large multi-agent system that uses learning to help office workers cope with email overload and to complete routine tasks more efficiently. RADAR provides a mixed-initiative user interface in which artificial intelligence helps users perform the tasks that arrive in email messages. A large-scale user test of RADAR demonstrated the effectiveness of its user interface and AI.

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.

article Free Access Share on Beyond the interface metaphor Author: Ken Mohnkern View Profile Authors Info & Claims ACM SIGCHI BulletinVolume 29Issue 2April 1997pp 11–15https://doi.org/10.1145/255065.255067Published:01 April 1997Publication History 9citation140DownloadsMetricsTotal Citations9Total Downloads140Last 12 Months50Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteeReaderPDF