Extracting Knowledge about Users' Activities from Raw Workstation Contents
by Mitchell, T., Wang, S., Huang, Y., and Cheyer, A.
The Twenty-First National Conference on Artificial Intelligence (AAAI ’06), July 2006.
Abstract
A long-standing goal of AI is the development of
intelligent workstation-based personal agents to assist
users in their daily lives. A key impediment to this goal
is the unrealistic cost of developing and maintaining a
detailed knowledge base describing the user’s different
activities, and which people, meetings, emails, etc. are
affiliated with each such activity. This paper presents a
clustering approach to automatically acquiring such a
knowledge base by analyzing the raw contents of the
workstation, including emails, contact person names, and
online calendar meetings. Our approach analyzes the
distribution of email words, the social network of email
senders and recipients, and the results of Google Desktop
Search queried with text from online calendar entries and
person contact names. For each cluster it constructs, the
program outputs a frame-based representation of the
corresponding user activity. This paper describes our
approach and experimentally assesses its performance
over the workstations of three different users.
Cognitive Assistant that Learns and Organizes
As part of DARPA’s Perceptive Agent that Learns (PAL) program, SRI and team members are working on developing a next-generation "Cognitive Agent that Learns and Organizes" (CALO).
