User modeling in adaptive interfaces
UM '99 Proceedings of the seventh international conference on User modeling
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Active preference learning for personalized calendar scheduling assistance
Proceedings of the 10th international conference on Intelligent user interfaces
A covenant with transparency: opening the black box of models
Communications of the ACM - Adaptive complex enterprises
IEEE Transactions on Knowledge and Data Engineering
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Toward establishing trust in adaptive agents
Proceedings of the 13th international conference on Intelligent user interfaces
Why and why not explanations improve the intelligibility of context-aware intelligent systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Taking advice from intelligent systems: the double-edged sword of explanations
Proceedings of the 16th international conference on Intelligent user interfaces
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Within an Air Operations Center (AOC), planners make crucial decisions to create the air plan for any given day. They are expected to complete the plan in part by pairing targeting or collection tasks with the available platforms. Any assistance these planners can acquire to help create the plan in a timely manner would make the entire process more efficient and effective. This paper describes the Intelligent Pairing Assistant (IPA) prototype, which would provide pairing recommendations at specific decision points in the planning process. IPA is designed as a plug-in for software systems already in use within AOCs. The primary contribution described in this paper is the application of existing research in intelligent user interfaces to a novel domain.