Interactive Artificial Intelligent(AI) assistant systems are designed to
offer timely guidance to help human users to complete a variety tasks. One of
the remaining challenges is to understand user's mental states during the task
for more personalized guidance. In this work, we analyze users' mental states
during task executions and investigate the capabilities and challenges for
large language models to interpret user profiles for more personalized user
guidance.

交互式人工智能辅助系统旨在提供及时指导，帮助人类用户完成各种任务。本研究分析了用户在任务执行过程中的心理状态，并研究了大型语言模型在解析用户个人资料以实现个性化用户指导方面的能力和挑战。

交互式人工智能助手系统中的用户建模挑战

User Modeling Challenges in Interactive AI Assistant Systems

Consider an assistive system that guides visually impaired users through
speech and haptic feedback to their destination. Existing robotic and
ubiquitous navigation technologies (e.g., portable, ground, or wearable
systems) often operate in a generic, user-agnostic manner. However, to minimize
confusion and navigation errors, our real-world analysis reveals a crucial need
to adapt the instructional guidance across different end-users with diverse
mobility skills. To address this practical issue in scalable system design, we
propose a novel model-based reinforcement learning framework for personalizing
the system-user interaction experience. When incrementally adapting the system
to new users, we propose to use a weighted experts model for addressing
data-efficiency limitations in transfer learning with deep models. A real-world
dataset of navigation by blind users is used to show that the proposed approach
allows for (1) more accurate long-term human behavior prediction (up to 20
seconds into the future) through improved reasoning over personal mobility
characteristics, interaction with surrounding obstacles, and the current
navigation goal, and (2) quick adaptation at the onset of learning, when data
is limited.

提出了一种基于强化学习的个性化导航系统，根据不同视力障碍者的个人特点和行动能力，通过权重专家模型逐步调整系统用户交互体验，实现更精准的长期行为预测和快速适应性学习。