Multi-User Personalisation in Human-Robot Interaction: Using Quantitative Bipolar Argumentation Frameworks for Preferences Conflict Resolution

2025-11-05

AI Summary

Existing personalized human-robot interaction research predominantly focuses on single-user adaptation, failing to resolve preference conflicts among multiple stakeholders. This paper proposes a multi-user personalization method grounded in Quantitative Bipolar Argumentation Frameworks (QBAFs), the first to incorporate dynamic environmental observations into QBAF. By jointly integrating user claims and the robot’s real-time environmental perception, the approach enables context-sensitive, adaptive decision-making. The method unifies preference modeling, dynamic evidence integration, and iterative argument strength updating, augmented by sensitivity analysis to quantify how input variations and contextual shifts affect decisions. Empirically evaluated in an assistive robot application for vulnerability assessment, the framework effectively mediates conflicting preferences between caregivers and care recipients, delivering transparent, interpretable, and responsive decision support—overcoming key limitations of static argumentation models and purely data-driven approaches.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Intelligent Robots: Human-Robot Interaction
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Resolving conflicting preferences in multi-user human-robot interaction
• Integrating user arguments with dynamic robot environmental observations
• Providing transparent conflict resolution for caregiver-care recipient scenarios

Innovation

• Framework resolves multi-user preference conflicts
• Dynamic robot observations adapt argument strength
• Transparent structured approach for competing preferences