RADAR: a Personal Assistant That Learns to Reduce Email Overload


Michael Freed, Jaime Carbonell, Geoffrey Gordon, Jordan Hayes, Brad A. Myers, Daniel Siewiorek, Stephen Smith, Aaron Steinfeld and Anthony Tomasic Conference Paper, Proceedings of 23rd National Conference on Artificial Intelligence (AAAI ’08), Vol. 3, pp. 1287 – 1293, July, 2008


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.

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