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 em ployed to infer a user's needs by consider ing 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) gain ing access to a stream of events from soft ware applications, (3) developing a language for transforming system events into observa tional 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 proto types served as the basis for the Office Assis tant in the Microsoft Office '97 suite of pro ductivity applications. Uncertainty is ubiquitous in attempts to recognize an agent's goals from observations of behavior. The Lumiere project at Microsoft Research has been fo cused on leveraging methods for reasoning under un certainty about the goals of software users. At the heart of Lumiere research and prototypes are Bayesian user models that capture the uncertain relationships among the goals and needs of a user and observations about program state, sequences of actions over time, and words in a user's query. Motivating problems in clude the computation and use of probability distri butions over a user's goals for providing appropriate assistance or automated services, for dynamically tai loring language models in speech recognition, and for appropriately guiding the allocation of computational resources in an operating system. We present challenges with the construction of key components of the Lumiere/Excel prototype for the Excel spreadsheet application. The Lumiere project was initiated in 1993, and the initial Lumiere/Excel prototype was first demonstrated to the Microsoft Of fice division in January 1994. Research on new appli cations and extensions continued in parallel with ef forts to integrate portions of the prototype into com mercially available software applications. In January 1997, a derivative of Lumiere research shipped as the Office Assistant in Microsoft Office '97 applications. We will describe key issues with user modeling and provide an overview of Bayesian reasoning and deci sion making about user needs. Then, we will discuss user modeling for identifying the needs of users work ing with desktop productivity software. We will re view the components and overall architecture of the Lumiere system. Finally, we will discuss the influence of Lumiere research on software products. 2 We shall focus on the use of Bayesian networks and influence diagrams in embedded applications to make inferences about the goals of users-and to take ideal actions based on probability distributions over these goals. We found that Bayesian models can be effective in diagnosing a user's needs and can provide useful en hancements to legacy software applications when em bedded within these programs. Additionally, Bayesian user models can provide an infrastructure for building new kinds of services and applications in software.
Horvitz et.al took a Bayesian approach to model whether a user needs assistance based on user actions and attributes and used it to provide assistance to user in a spreadsheet application REF ].
The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
ArXiv
ArXiv
Computer Science