The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users

Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July 1998, Morgan Kaufmann: San Francisco. |

Publication

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 user’s 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’s expertise, and (5) the development of an overall architecture for an intelligent user interface. Lumiere prototypes served as the basis for components of the Office Assistant in the Microsoft Office ’97 suite of productivity applications.

Demonstration of Lumiere (1995)

Bayesian User Modeling for Inferring the Goals and Needs of Software Users In this 1995 video from Microsoft Research, Eric Horvitz demonstrates the Lumiere system. The Lumiere Project was an early exploration of using probability and expected utility to provide assistance to computer software users. The project centered on performing inferences about a computer user's 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…