Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user interfaces is the reliance on high-quality user data that has to be collected offline for each task. We formulate UI adaptation as a multi-agent reinforcement learning problem to overcome this challenge. In our formulation, a user agent mimics a real user and learns to interact with a UI. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent learns the task structure from the user agent's behavior and, based on that, can support the user agent in completing its task. Our method produces adaptation policies that are learned in simulation only and, therefore, does not need real user data. Our experiments show that learned policies generalize to real users and achieve on par performance with data-driven supervised learning baselines.

翻译：自适应用户界面（Adaptive User Interfaces）能够自动调整界面以更好地支持用户完成任务。近年来，机器学习技术促使自适应界面转向更强大、更复杂的实现方式。然而，自适应用户界面面临的核心挑战在于其依赖高质量用户数据，而此类数据必须针对每个任务进行离线收集。为克服这一挑战，我们将界面自适应问题形式化为多智能体强化学习框架。在该框架中，用户智能体模拟真实用户的行为并学习与界面交互；与此同时，界面智能体通过自适应调整界面来最大化用户智能体的任务完成效能。界面智能体从用户智能体的行为中学习任务结构，并据此支持用户智能体完成任务。该方法仅通过仿真环境学习自适应策略，因此无需真实用户数据。实验表明，学习所得策略能够泛化至真实用户，其表现与基于数据驱动的监督学习基线方法相当。