让AI通过对话动态学习用户思维，实现个性化交互。
Does Your AI Agent Get You? A Personalizable Framework for Approximating Human Models from Argumentation-based Dialogue Traces
- 基于论证对话，用概率加权与贝叶斯更新动态建模用户
- 实验显示能准确捕捉用户信念变化，优于现有方法
- 适合需要持续理解用户立场的交互式AI系统
可解释AI正越来越多地采用论证方法来促进AI代理与人类用户之间的互动解释。尽管现有方法通常依赖预设的人类用户模型，但在交互过程中动态学习和更新这些模型方面仍存在关键空白。本文提出一种框架，使AI代理能够通过基于论证的对话来调整对人类用户的理解。该方法名为Persona，借鉴前景理论，将概率权重函数与贝叶斯信念更新机制相结合，根据交流的论点不断优化对可能人类模型的概率分布。在应用论证场景中对真实用户进行的实证评估表明，Persona能有效捕捉不断演变的人类信念，促进个性化交互，并优于当前最先进的方法。
原文摘要 · Abstract (English)
Explainable AI is increasingly employing argumentation methods to facilitate interactive explanations between AI agents and human users. While existing approaches typically rely on predetermined human user models, there remains a critical gap in dynamically learning and updating these models during interactions. In this paper, we present a framework that enables AI agents to adapt their understanding of human users through argumentation-based dialogues. Our approach, called Persona, draws on prospect theory and integrates a probability weighting function with a Bayesian belief update mechanism that refines a probability distribution over possible human models based on exchanged arguments. Through empirical evaluations with human users in an applied argumentation setting, we demonstrate that Persona effectively captures evolving human beliefs, facilitates personalized interactions, and outperforms state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。