Building a Stochastic Dynamic Model of Application Use

Abstract

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 sub-states 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.