Modeling sequences of user actions for statistical goal recognition

Abstract

User goals are of major importance for an interface agent because they serve as a context to define what the user’s focus of attention is at a givenmoment. The user’s goals should be detected as soon as possible, after observing few user actions, in order to provide the userwith timely assistance. In this article,we describe an approach for modeling and recognizing user goals from observed sequences of user actions by using Variable Order Markov models combined with an exponential moving average (EMA) on the prediction probabilities. The validity of our approach has been tested using data collected from real users in the Unix domain. The results obtained show that an interface agent can achieve near 90% average accuracy and over 58% online accuracy in predicting the most probable user goal after each observed action, in a time linear to the number of goals being modeled. We also found that the use of an EMA allows a faster convergence in the actual user goal.