Cascaded generic XCS to learn about reminding preferences

We are developing an adaptive reminding system, which learns when and how to present notifications. In this paper, we focus on our XCS-based model, composed of two cascaded sets of classifiers: the first one learns a categorization of calendar data, while the second selects the appropriate forms of combinable reminders depending on the user and device contexts. After describing the characteristics of the input data, we present the extensions we propose to provide a generic XCS architecture, which seems suitable for processing those specific inputs. Finally, we describe our user feedback mechanism, and the according reward system. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: Artificial Intelligence— Learning General Terms Algorithms Keywords Learning Classifier Systems, personal time management, adaptive reminders

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Nadine Richard, Samuel Tardieu, Seiji Yamada
• Keywords: Adaptive Reminding System, Combinable Reminders, GECCO 2007, User Feedback Mechanism