MARLUI: Multi-Agent Reinforcement Learning for Goal-Agnostic Adaptive UIs

The goal of Adaptive UIs is to automatically change an interface so that the UI better supports users in their tasks. A core challenge is to infer user intent from user input and chose adaptations accordingly. Designing effective online UI adaptations is challenging because it relies on tediously hand-crafted rules or carefully collected, high-quality user data. To overcome these challenges, we formulate UI adaptation as a multi-agent reinforcement learning problem. In our formulation, a user agent learns to interact with a UI to complete a task. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent is agnostic to the goal. It learns the task structure from the behavior of the user agent and based on that can support the user agent in completing its task. We show that our approach leads to a significant reduction in necessary number of actions on a photo editing task in silico. Furthermore, our user studies demonstrate the generalization capabilities of our interface agent from a simulated user agent to real users.