  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.
