A Self-Negotiation Framework for Ethical Decision-Making during Task Interruptions in Service Robots

2026-06-30

AI Summary

This work addresses the challenge of task interruption conflicts in service robots operating in public spaces, where concurrent requests from multiple users necessitate autonomous ethical decision-making without external coordination. The authors propose a self-negotiation framework that constructs individualized ethical profiles for each user, encoding contextual ethical preferences and constraints, enabling the robot to conduct internal multilateral negotiation to resolve conflicts. This approach pioneers a dynamic, centerless multilateral negotiation mechanism within a single-robot system, overcoming limitations of static rule-based systems or those reliant on external arbitration. Implemented on a modular ROS architecture, the system efficiently generates ethically consistent decisions aligned with user preferences in simulation, supports real-time multi-user negotiation with sub-1.5-second response times, and exhibits near-linear computational overhead growth with increasing user count.

Abstract

Service robots operating in public environments frequently encounter interruptions when multiple users request service simultaneously. Resolving such conflicts requires ethical decision-making, as prioritizing one user request can disadvantage another. Current approaches rely on static rules or centralized arbitration and do not support autonomous, ethics-based conflict resolution. This paper addresses the question of how a single robot can arbitrate between multiple users during task interruptions and make ethically aligned decisions without relying on external coordination. We introduce a self-negotiation framework that represents each user by an ethical profile that captures their contextual ethical preferences and conditions, and resolves conflicts through an internal negotiation process. The framework is implemented in a modular ROS-based implementation and evaluated in simulation with a realistic interruption scenario. The results show that the system consistently produces user ethical preference-aligned outcomes, supports multilateral negotiation among users, and responds within 1.5 seconds, with near-linear runtime growth under increasing user input.

Problem

• ethical decision-making
• task interruptions
• service robots
• conflict resolution
• autonomous arbitration

Innovation

• self-negotiation
• ethical decision-making
• service robots
• task interruption
• ethical profiling