Uncertainty in Artificial Intelligence
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The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
Eric Horvitz, John Breese, David Heckerman, David Hovel, Koos Rommelse
Abstract:
The Lumiere Project centers on harnessing probability and utility to provide assistance to computer software users. We review work on Bayesian user models that can be employed to infer a users needs by considering a user's background, actions, and queries. Several problems were tackled in Lumiere research, including (1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gaining access to a stream of events from software applications, (3) developing a language for transforming system events into observational variables represented in Bayesian user models, (4) developing persistent profiles to capture changes in a user expertise, and (5) the development of an overall architecture for an intelligent user interface. Lumiere prototypes served as the basis for the Office Assistant in the Microsoft Office '97 suite of productivity applications.
Keywords: Bayesian user modeling, goal recognition, information retrieval, intelligent agents,
Pages: 256-265
PS Link: http://research.microsoft.com/~horvitz/lumiere.htm
PDF Link: /papers/98/p256-horvitz.pdf
BibTex:
@INPROCEEDINGS{Horvitz98,
AUTHOR = "Eric Horvitz and John Breese and David Heckerman and David Hovel and Koos Rommelse",
TITLE = "The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "256--265"
}


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