A probabilistic mental model for estimating disruption

摘要

Adaptive software systems are intended to modify their appearance, performance or functionality to the needs and preferences of different users. A key bottleneck in building effective adaptive systems is accounting for the cost of disruption to a user's mental model of the application caused by the system's adaptive behaviour. In this work, we propose a probabilistic approach to modeling the cost of disruption. This allows an adaptive system to tradeoff disruption cost with expected savings (or other benefits) induced by a potential adaptation in a principled, decision-theoretic fashion. We conducted two experiments with 48 participants to learn model parameters in an adaptive menu selection environment. We demonstrate the utility of our approach in simulation and usability studies. Usability results with 8 participants suggest that our approach is competitive with other adaptive menus w.r.t. task performance, while providing the ability to reduce disruption and adapt to user preferences.