Adapting user interfaces (UIs) requires taking into account both positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs — for example, due to surprise or relearning effort. It is essential to consider differences between users as the effect of an adaptation depends on the user's strategies, e.g. how each user searches for information in a UI. This microproject extends an earlier collaboration between partners on model-based reinforcement learning for adaptive UIs by developing methods to account for individual differences.

We first developed computational models to explain and predict users' visual search and pointing strategies when searching within a UI. We applied this model to infer user strategies based on interaction history and adapt UIs accordingly.