RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robots

2025-07-30

AI Summary

Autonomous systems struggle to integrate user-specific ethical preferences, resulting in low trustworthiness and poor moral adaptability; multi-system interaction further necessitates ethical negotiation to achieve consensus. This paper proposes the first domain-agnostic ethical negotiation reference architecture, enabling multiple robots to dynamically fuse individual ethical models with contextual information for real-time, distributed ethical negotiation—particularly under resource conflicts—in dynamic environments. Our approach integrates rule-based ethical modeling, context-aware mechanisms, and a lightweight negotiation protocol, implemented via the RobEthiChor framework on ROS for real-robot deployment. Experiments demonstrate that the system achieves ethical consensus in over 73% of scenarios, with an average negotiation latency of only 0.67 seconds, validating its feasibility, efficiency, and practical deployability.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Intelligent Robots: Multi-Robot Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

The presence of autonomous systems is growing at a fast pace and it is impacting many aspects of our lives. Designed to learn and act independently, these systems operate and perform decision-making without human intervention. However, they lack the ability to incorporate users' ethical preferences, which are unique for each individual in society and are required to personalize the decision-making processes. This reduces user trust and prevents autonomous systems from behaving according to the moral beliefs of their end-users. When multiple systems interact with differing ethical preferences, they must negotiate to reach an agreement that satisfies the ethical beliefs of all the parties involved and adjust their behavior consequently. To address this challenge, this paper proposes RobEthiChor, an approach that enables autonomous systems to incorporate user ethical preferences and contextual factors into their decision-making through ethics-based negotiation. RobEthiChor features a domain-agnostic reference architecture for designing autonomous systems capable of ethic-based negotiating. The paper also presents RobEthiChor-Ros, an implementation of RobEthiChor within the Robot Operating System (ROS), which can be deployed on robots to provide them with ethics-based negotiation capabilities. To evaluate our approach, we deployed RobEthiChor-Ros on real robots and ran scenarios where a pair of robots negotiate upon resource contention. Experimental results demonstrate the feasibility and effectiveness of the system in realizing ethics-based negotiation. RobEthiChor allowed robots to reach an agreement in more than 73% of the scenarios with an acceptable negotiation time (0.67s on average). Experiments also demonstrate that the negotiation approach implemented in RobEthiChor is scalable.

Problem

• Incorporating user ethical preferences into autonomous robot decision-making
• Enabling robots to negotiate based on differing ethical beliefs
• Providing scalable ethics-based negotiation for real-world robot interactions

Innovation

• Automated context-aware ethics-based negotiation system
• Domain-agnostic architecture for ethical decision-making
• ROS implementation enabling robot ethics negotiation