HumAIne-Chatbot：基于强化学习的实时个性化对话式人工智能
HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning
AI总结：
针对现有对话系统缺乏个性化的问题，提出HumAIne-chatbot，通过GPT虚拟人设预训练和在线强化学习动态建模用户画像，实现实时个性化对话，实验表明显著提升满意度与任务完成度。
摘要：
当前的对话式人工智能系统通常提供通用的、一刀切的交互，忽视了用户的个体特征，并且缺乏自适应的对话管理。为了解决这一不足，我们引入了HumAIne-chatbot，一种由人工智能驱动的对话代理，通过新颖的用户画像框架实现个性化响应。该系统在多种由GPT生成的虚拟人设上进行预训练，以建立对用户类型的广泛先验。在实时交互过程中，在线强化学习代理通过结合隐式信号（如打字速度、情感、参与时长）和显式反馈（如喜欢和不喜欢）来细化每个用户的模型。该画像动态地指导聊天机器人的对话策略，实现内容和风格的实时适应。为评估该系统，我们在多个对话领域中使用50个合成人设进行了受控实验。结果表明，当启用个性化功能时，用户满意度、个性化准确性和任务完成度均持续提升。统计分析确认了个性化与非个性化条件之间存在显著差异，关键指标上具有较大的效应量。这些发现凸显了人工智能驱动的用户画像的有效性，并为未来的实际验证奠定了坚实基础。
英文摘要：
Current conversational AI systems often provide generic, one-size-fits-all interactions that overlook individual user characteristics and lack adaptive dialogue management. To address this gap, we introduce \textbf{HumAIne-chatbot}, an AI-driven conversational agent that personalizes responses through a novel user profiling framework. The system is pre-trained on a diverse set of GPT-generated virtual personas to establish a broad prior over user types. During live interactions, an online reinforcement learning agent refines per-user models by combining implicit signals (e.g. typing speed, sentiment, engagement duration) with explicit feedback (e.g., likes and dislikes). This profile dynamically informs the chatbot dialogue policy, enabling real-time adaptation of both content and style. To evaluate the system, we performed controlled experiments with 50 synthetic personas in multiple conversation domains. The results showed consistent improvements in user satisfaction, personalization accuracy, and task achievement when personalization features were enabled. Statistical analysis confirmed significant differences between personalized and nonpersonalized conditions, with large effect sizes across key metrics. These findings highlight the effectiveness of AI-driven user profiling and provide a strong foundation for future real-world validation.
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。
