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UAI 1999

Attention-Sensitive Alerting

Conference Paper Accepted Paper Artificial Intelligence · Machine Learning · Uncertainty in Artificial Intelligence

Abstract

We introduce utility-directed procedures for mediating the flow of potentially distracting alerts and communications to computer users. We present models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption. We describe the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications. After introducing principles of attention-sensitive alerting, we focus on the problem of guiding alerts about email messages. We dwell on the problem of inferring the expected criticality of email and discuss work on the Priorities system, centering on prioritizing email by criticality and modulating the communication of notifications to users about the presence and nature of incoming email.

Authors

Keywords

  • User modeling
  • models of attention
  • Bayesian network
  • human-computer interaction
  • int

Context

Venue
Conference on Uncertainty in Artificial Intelligence
Archive span
1985-2025
Indexed papers
3717
Paper id
502075765108727612