Building User Models for Predictive Agents from Human-Computer Interactions

摘要

The benefits of using user models are explored for predictive agents in a real world application. Interface agents are designed for the prediction of resource usage in the UNIX domain using a hybrid approach to automatically acquire regularities of user behavior. Both sequential information from the command sequence and relational information such as system's responses and arguments to the commands are considered to typify a user's behavior and intentions. Issues of ambiguity, distraction and interleaved execution of user behavior are examined and taken into account to improve the probability estimation in hidden Markov models.