Active preference learning for personalized calendar scheduling assistance

We present PLIANT, a learning system that supports adaptive assistance in an open calendaring system. PLIANT learns user preferences from the feedback that naturally occurs during interactive scheduling. It contributes a novel application of active learning in a domain where the choice of candidate schedules to present to the user must balance usefulness to the learning module with immediate benefit to the user. Our experimental results provide evidence of PLIANT’s ability to learn user preferences under various conditions and reveal the tradeoffs made by the different active learning selection strategies. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning – parameter learning I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search – scheduling General Terms Algorithms, Performance, Design, Experimentation, Human Factors Keywords adaptive user interfaces, machine learning, active learning, learning preferences

• Type: Conference
• Year: 2005
• Where: IUI
• Authors: Melinda T. Gervasio, Michael D. Moffitt, Martha E. Pollack, Joseph M. Taylor, Tomás E. Uribe
• Keywords: Active Learning, IUI 2005, Learning Module, Software Engineering, User Preferences