【学术报告】TaskTracer Machine Learning Applied to Improve Desktop Computing

发稿时间:2009-11-01浏览次数:1483

题目:
TaskTracer Machine Learning Applied to Improve Desktop Computing

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

时间:
11月5日(星期四) 上午9:30-10:30

地点:
蒙民伟楼404会议室

摘要:
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 projectsactivities and declares to TaskTracer what
current task heshe 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 FolderPredictor (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.


简历:
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.