Navigation adaptative dans les systèmes interactifs : paradigme et solution

Abstract

As part of the customization of man-machine interfaces, we present in this paper a generic paradigm of dynamic adaptation of the interface to the user's navigation habits. This involves providing the current user with automated assistance in carrying out his usual tasks, so as to make him more efficient, in particular by reducing his mental load or the number of his actions. This adaptive paradigm, which is not specific to an application, combines in a flexible way: adaptive guidance, adaptive shortcuts, and adaptive automatisms. These different modes of adaptivity are managed by the system during the interaction, in a reliable and reactive manner on the basis of adaptive machine learning leveraging Bayesian theory and dealing with uncertainty in a rigorous way. The architecture of the solution is based on a predictive navigation model underpinned by a finite state machine, and integrating adaptive mechanisms. Our paradigm thus integrates a user activity model, a navigation task model within an interface, and an adaptive interaction model. It offers dynamic navigation assistance by jointly optimizing the nature of the adaptations and the distribution of initiatives between the user and the system, depending on the part of the interface explored and its use. We will endeavor to describe and justify this paradigm from ergonomic and technical angles, supported by a demonstrator running on a touchscreen tablet.