Project Home Page
Principal Investigators: Tom
Mitchell and Katia
Sycara, CMU School
of Computer Science
Our objective is to create and demonstrate
- machine learning methods that allow personal software agents to automatically
customize to the needs of their users, and
- methods for automated negotiation among these agents, in order to improve
their effectiveness, robustness, scalability and maintainability.
In order to explore these issues, we have developed a collection of
various software agents:
- a Calendar Apprentice (CAP) which learns users' scheduling preferences
(see our most
recent paper, or click
here for access to some of the data used in our experiments),
- a Mosaic-based netnews reader (NewsWeeder) that learns users' reading
interests ( try
- a Mosaic-based tour guide (WebWatcher) that helps you search for information,
and learns from experience. See the project
- a Visitorhost
which helps schedule visitors for technical briefings, and
- a Personnel
Information mediator which provides information about specific individuals
and their jobs.
The Pleiades system
diagram illustrates the interactions among these agents.
See our list of publications.
Research support: Arpa, Digital,
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Last Updated: 11/12/96