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Seminar abstract

TaskTracer Machine Learning Applied to Improve Desktop Computing

Thomas Dietterich
School of Electrical Engineering and Computer Science, Oregon State University, USA

Abstract :

Knowledge workers are multi-taskers. Their work lives can be divided into multiple on-going projects or activities, and their time at the desktop interleaves work on these projects and activities. However, existing desktop user interfaces do not have any notion of coherent projects or activities. The TaskTracer system seeks to support these workers by organizing the files, folders, contact information, and web sites (collectively known as "resources") according to the activities that they support. To use TaskTracer, the user defines a hierarchy of projects/activities and declares to TaskTracer what current task he/she is working on at each point in time. TaskTracer instruments Microsoft Windows and Office to gather data on the resources that are accessed by the user and associates them with the currently-declared task. It then provides project-related assistance through (a) the TaskExplorer (which makes it easy for the user to return to previously-accessed resources), (b) the Folder/Predictor (which predicts the relevant folder for Open and SaveAs actions), and TaskNotes (which provides a task-related notebook). To reduce the need for the user to declare the current activity, we apply machine learning methods to predict the current activity of the user based on incoming email messages and desktop behavior. This talk will describe the tasktracer system and discuss what properties make a machine learning algorithm best for online learning in user interfaces.


Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. In 1987, he was named a Presidential Young Investigator for the US National Science Foundation. In 1990, he published, with Dr. Jude Shavlik, the book entitled Readings in Machine Learning, and he also served as the Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90). From 1992-1998 he held the position of Executive Editor of the journal Machine Learning. He is a Fellow of the American Association for the Advancement of Science, the Association for the Advancement of Artificial Intelligence, and the Association for Computing Machinery In 2000, he co-founded a new, free electronic journal: The Journal of Machine Learning Research. He served as Technical Program Chair of the Neural Information Processing Systems (NIPS) conference in 2000 and General Chair in 2001. He is Past-President of the International Machine Learning Society, a member of the IMLS Board, and he also serves on the Board of Advisors of the NIPS Foundation.
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