Adapting User Interfaces with Model-based Reinforcement Learning
This addresses the challenge of adapting interfaces without imposing high costs on users, such as surprise or relearning effort, which is an incremental improvement in HCI and AI integration.
The paper tackles the problem of adaptive user interfaces by proposing a model-based reinforcement learning method that plans sequences of adaptations using predictive HCI models to estimate effects, showing it outperforms non-adaptive and frequency-based policies in the case of adaptive menus.
Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user -- for example, due to surprise or relearning effort -- or "trap" the process to a suboptimal design immaturely. However, effects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It finds beneficial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning method plans sequences of adaptations and consults predictive HCI models to estimate their effects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy.