Abstract

Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user’s current interruptability from computer activity and relevant contextual information. We describe a system that intermittently asks users to assess their perceived interruptability during a training phase and that builds decision-theoretic models with the ability to predict the cost of interrupting the user. The models are used at run-time to compute the expected cost of interruptions, providing a mediator for incoming notifications, based on a consideration of a user’s current and recent history of computer activity, meeting status, location, time of day, and whether a conversation is detected.

Citations

152	A Bayesian approach to learning Bayesian networks with local structure - Chickering, Heckerman, et al. - 1997
129	What makes interruptions disruptive? A study of length, similarity, and complexity - Gillie - 1989
101	Learning and Reasoning about Interruption - Horvitz, Apacible - 2003
101	Interruption of people in human-computer interaction. Doctoral dissertation - McFarlane - 1998
71	Examining the Robustness of Sensor-Based Statistical Models of Human Interruptibility - Fogarty, Hudson, et al. - 2004