Reasoning about ideal interruptible moments: A soft computing implementation of an interruption classifier in free-form task environments

This research shows: (1) the classifier incorporates a user model in its' reasoning process. Most of the research in this area has focused on task-based contextual information when designing systems that reason about interruptions; (2) the classifier performed at 96% accuracy in experimental test scenarios and significantly outperformed other comparable systems; (3) the classifier is implemented using an advanced machine learning technology-an Adaptive Neural-Fuzzy Inference System-this is unique since all other systems use Bayesian Networks or other machine learning tools; (4) the classifier does not require any direct user involvement-in other systems, users must provide interruption annotations while reviewing video sessions so the system can learn; and (5) a promising direction for reasoning about interruptions for free-form tasks-this is largely an unsolved problem.