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 the Twenty-Third AAAI Conference on Artificial Intelligence, pp. 1287-1293, July, 2008

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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.

author = {Michael Freed and Jaime Carbonell and Geoffrey Gordon and Jordan Hayes and Brad A. Myers and Daniel Siewiorek and Stephen Smith and Aaron Steinfeld and Anthony Tomasic},
title = {RADAR: A Personal Assistant that Learns to Reduce Email Overload},
booktitle = {Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence},
year = {2008},
month = {July},
pages = {1287-1293},
keywords = {Software Agents, Machine Learning and Discovery},
} 2017-09-13T10:41:32-04:00