The continuous adaptation of software systems to meet the evolving needs of users is very important for enhancing user experience (UX). User interface (UI) adaptation, which involves adjusting the layout, navigation, and content presentation based on user preferences and contextual conditions, plays an important role in achieving this goal. However, suggesting the right adaptation at the right time and in the right place remains a challenge in order to make it valuable for the end-user. To tackle this challenge, machine learning approaches could be used. In particular, we are using Reinforcement Learning (RL) due to its ability to learn from interaction with the users. In this approach, the feedback is very important and the use of physiological data could be benefitial to obtain objective insights into how users are reacting to the different adaptations. Thus, in this PhD thesis, we propose an RL-based UI adaptation framework that uses physiological data. The framework aims to learn from user interactions and make informed adaptations to improve UX. To this end, our research aims to answer the following questions: Does the use of an RL-based approach improve UX? How effective is RL in guiding UI adaptation? and Can physiological data support UI adaptation for enhancing UX? The evaluation plan involves conducting user studies to evaluate answer these questions. The empirical evaluation will provide a strong empirical foundation for building, evaluating, and improving the proposed adaptation framework. The expected contributions of this research include the development of a novel framework for intelligent Adaptive UIs, insights into the effectiveness of RL algorithms in guiding UI adaptation, the integration of physiological data as objective measures of UX, and empirical validation of the proposed framework's impact on UX.

翻译：软件系统持续适应用户不断变化的需求对于提升用户体验至关重要。用户界面自适应涉及根据用户偏好和情境条件调整布局、导航和内容呈现，在实现该目标中扮演重要角色。然而，在正确的时间、正确的位置提出恰当的自适应方案仍是让终端用户受益的关键挑战。为攻克这一难题，可采用机器学习方法。具体而言，我们利用强化学习因其具备从用户交互中学习的能力。在该方法中，用户反馈至关重要，而使用生理数据有助于获取用户对不同自适应方案反应的客观洞见。因此，本博士论文提出一个基于强化学习的用户界面自适应框架，该框架利用生理数据，旨在从用户交互中学习并做出明智的自适应决策以改善用户体验。为此，本研究拟回答以下问题：基于强化学习的方法能否提升用户体验？强化学习在引导用户界面自适应方面效果如何？生理数据能否支持用户界面自适应以增强用户体验？评估计划通过开展用户研究来回答这些问题。实证评估将为构建、评估和改进所提出的自适应框架提供坚实的实证基础。本研究预期贡献包括：开发新型智能自适应用户界面框架、揭示强化学习算法在引导用户界面自适应中的有效性、整合生理数据作为用户体验的客观度量指标，以及通过实证验证所提框架对用户体验的影响。