基于强化学习的用户界面智能适配框架
Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces
AI总结：
针对用户界面智能适配时机与成效评估难题，提出基于强化学习的适配框架，用预测型HCI模型评估动作、OpenAI Gym扩展实现，可训练智能体最大化用户参与度。
摘要：
软件系统的用户界面（UI）适配以满足用户需求和偏好是一项复杂任务。核心挑战在于在恰当的时机提供恰当的适配，为终端用户创造价值。机器学习（ML）技术的最新进展可为适配过程提供有效支撑手段。本文提出一种智能用户界面适配参考框架的实例化方案，采用强化学习（RL）作为机器学习组件实现用户界面适配，最终提升整体用户体验（UX）。借助强化学习，系统能够从过往适配中学习，优化决策能力。此外，评估这类适配的成效仍是一项挑战。为解决该问题，我们提出使用预测型人机交互（HCI）模型来评估RL智能体执行的每一项动作（即适配操作）的结果。我们还展示了该实例化框架的实现，它是OpenAI Gym的扩展，可作为开发和比较强化学习算法的工具包。该Gym环境具备高度可配置性，可扩展至其他UI适配场景。评估结果表明，我们的基于RL的框架能够成功训练RL智能体，使其学会在特定场景下适配UI，通过使用HCI模型作为奖励预测器，最大化用户参与度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
