RADAR: A Personal Assistant that Learns to Reduce Email Overload

Michael Freed, Jaime Carbonell, Geoff Gordon, Jordan Hayes, Brad Myers, Daniel Siewiorek, Stephen Smith, Aaron Steinfeld, Anthony Tomasic

Email client software is widely used for personal task management, a purpose for which it was not designed and is poorly suited. Past attempts to remedy the problem have focused on adding task management features to the client UI. RADAR uses an alternative approach modeled on a trusted human assistant who reads mail, identifies task-relevant message content, and helps manage and execute tasks. This paper describes the integration of diverse AI technologies and presents results from human evaluation studies comparing RADAR user performance to unaided COTS tool users and users partnered with a human assistant. As machine learning plays a central role in many system components, we also compare versions of RADAR with and without learning. Our tests show a clear advantage for learning-enabled RADAR over all other test conditions.

Subjects: 7.2 Software Agents; 12. Machine Learning and Discovery

Submitted: Apr 15, 2008


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