Formal modelling of task interruptions
Conference Companion on Human Factors in Computing Systems
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Models of attention in computing and communication: from principles to applications
Communications of the ACM
Learning and reasoning about interruption
Proceedings of the 5th international conference on Multimodal interfaces
Proceedings of the 9th international conference on Intelligent user interfaces
BusyBody: creating and fielding personalized models of the cost of interruption
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Using task context variables for selecting the best timing for interrupting users
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Leveraging characteristics of task structure to predict the cost of interruption
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Scalable Parallel Implementation of Exact Inference in Bayesian Networks
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Disruption and recovery of computing tasks: field study, analysis, and directions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A parallel algorithm for learning Bayesian networks
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Towards automatic cognitive load measurement from speech analysis
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: interaction design and usability
A comparison of HMMs and dynamic bayesian networks for recognizing office activities
UM'05 Proceedings of the 10th international conference on User Modeling
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Intelligent software must frequently interrupt users, but the problem of deciding when to interrupt the user is still unsolved. In this paper, an algorithm is presented that identifies the appropriate times to interrupt the user is proposed and visualizations of the user’s state. The Intelligent Interruption Algorithm draws from user, task, and environmental contextual information dynamically extracted from the environment as the user performs computer based tasks. The visualizations are a representation of these complex components presented in a way that is meaningful for analysis purposes and for the user. This paper presents the following: the interruption algorithm; the machine learning algorithms used; and the preliminary results of visualizing the interruptible moment.