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Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. The main challenge is to provide the appropriate adaptations at the appropriate time to offer value to end-users. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component to adapt user interfaces and ultimately improving the overall User Experience (UX). By using RL, the system is able to learn from past adaptations to improve the decision-making capabilities. Moreover, assessing the success of such adaptations remains a challenge. To overcome this issue, we propose to use predictive Human-Computer Interaction (HCI) models to evaluate the outcome of each action (ie adaptations) performed by the RL agent. In addition, we present an implementation of the instantiated framework, which is an extension of OpenAI Gym, that serves as a toolkit for developing and comparing RL algorithms. This Gym environment is highly configurable and extensible to other UI adaptation contexts. The evaluation results show that our RL-based framework can successfully train RL agents able to learn how to adapt UIs in a specific context to maximize the user engagement by using an HCI model as rewards predictor., Comment: To be published in Companion of the16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS Companion '24). 9 pages, 2 figures, 28 references

Adapting the User Interface (UI) of software systems to user requirements and the context of use is challenging. The main difficulty consists of suggesting the right adaptation at the right time in the right place in order to make it valuable for end-users. We believe that recent progress in Machine Learning techniques provides useful ways in which to support adaptation more effectively. In particular, Reinforcement learning (RL) can be used to personalise interfaces for each context of use in order to improve the user experience (UX). However, determining the reward of each adaptation alternative is a challenge in RL for UI adaptation. Recent research has explored the use of reward models to address this challenge, but there is currently no empirical evidence on this type of model. In this paper, we propose a confirmatory study design that aims to investigate the effectiveness of two different approaches for the generation of reward models in the context of UI adaptation using RL: (1) by employing a reward model derived exclusively from predictive Human-Computer Interaction (HCI) models (HCI), and (2) by employing predictive HCI models augmented by Human Feedback (HCI&HF). The controlled experiment will use an AB/BA crossover design with two treatments: HCI and HCI&HF. We shall determine how the manipulation of these two treatments will affect the UX when interacting with adaptive user interfaces (AUI). The UX will be measured in terms of user engagement and user satisfaction, which will be operationalized by means of predictive HCI models and the Questionnaire for User Interaction Satisfaction (QUIS), respectively. By comparing the performance of two reward models in terms of their ability to adapt to user preferences with the purpose of improving the UX, our study contributes to the understanding of how reward modelling can facilitate UI adaptation using RL., Comment: 6 pages + 1 refs. 2 figures