While personalisation in Human-Robot Interaction (HRI) has advanced significantly, most existing approaches focus on single-user adaptation, overlooking scenarios involving multiple stakeholders with potentially conflicting preferences. To address this, we propose the Multi-User Preferences Quantitative Bipolar Argumentation Framework (MUP-QBAF), a novel multi-user personalisation framework based on Quantitative Bipolar Argumentation Frameworks (QBAFs) that explicitly models and resolves multi-user preference conflicts. Unlike prior work in Argumentation Frameworks, which typically assumes static inputs, our approach is tailored to robotics: it incorporates both users' arguments and the robot's dynamic observations of the environment, allowing the system to adapt over time and respond to changing contexts. Preferences, both positive and negative, are represented as arguments whose strength is recalculated iteratively based on new information. The framework's properties and capabilities are presented and validated through a realistic case study, where an assistive robot mediates between the conflicting preferences of a caregiver and a care recipient during a frailty assessment task. This evaluation further includes a sensitivity analysis of argument base scores, demonstrating how preference outcomes can be shaped by user input and contextual observations. By offering a transparent, structured, and context-sensitive approach to resolving competing user preferences, this work advances the field of multi-user HRI. It provides a principled alternative to data-driven methods, enabling robots to navigate conflicts in real-world environments.

翻译：尽管人机交互（HRI）中的个性化研究已取得显著进展，但现有方法大多聚焦于单用户适应，忽视了涉及多个利益相关者且可能存在偏好冲突的场景。为此，我们提出多用户偏好定量双极论证框架（MUP-QBAF），这是一种基于定量双极论证框架（QBAFs）的新型多用户个性化框架，能够显式建模并解决多用户偏好冲突。与通常假设静态输入的既有论证框架研究不同，本方法专为机器人系统定制：它同时整合用户论证与机器人对环境的动态观测，使系统能够随时间适应并响应变化的情境。正负向偏好均被表征为论证，其强度依据新信息进行迭代重计算。通过一个现实案例研究——在衰弱评估任务中辅助机器人调解护理人员与被护理者之间的冲突偏好——我们展示并验证了该框架的特性与能力。此项评估进一步包含对论证基础得分的敏感性分析，揭示了用户输入与情境观测如何影响偏好结果。通过提供一种透明、结构化且情境敏感的竞争性用户偏好解决方法，本工作推动了多用户人机交互领域的发展。它为数据驱动方法提供了原则性替代方案，使机器人能够在真实环境中有效应对冲突。