Adapting Pervasive Environments through Machine Learning and Dynamic Personalization

Abstract

Current pervasive environments should contain mechanisms, such as personalization, that adapt the environment to help the user meet their individual needs. However, manually creating, maintaining and utilizing a preference set is no easy task for a user, requiring continued time and effort. A more desirable approach is to implicitly build and maintain the preference set by using monitoring and learning mechanisms and apply such preferences when required on behalf of the user. This paper introduces the Daidalos Personalization and Learning system which monitors user behaviour and context to not only build and maintain dynamic preferences but also to apply them in a dynamic fashion. An example scenario is presented to demonstrate how such mechanisms are used to adapt a pervasive environment on a user¿s behalf.