Integrating user interface agents with conventional applications
IUI '98 Proceedings of the 3rd international conference on Intelligent user interfaces
Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
Machine Learning
Predicting Future User Actions by Observing Unmodified Applications
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Automatic recognition of learner groups in exploratory learning environments
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Modeling sequences of user actions for statistical goal recognition
User Modeling and User-Adapted Interaction
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Many intelligent user interfaces employ application and user models to determine the user's preferences, goals and likely future actions. Such models require application analysis, adaptation and expansion. Building and maintaining such models adds a substantial amount of time and labour to the application development cycle. We present a system that observes the interface of an unmodified application and records users' interactions with the application. From a history of such observations we build a coarse state space of observed interface states and actions between them. To refine the space, we hypothesize substates based upon the histories that led users to a given state. We evaluate the information gain of possible state splits, varying the length of the histories considered in such splits. In this way, we automatically produce a stochastic dynamic model of the application and of how it is used. To evaluate our approach, we present models derived from real-world application usage data.