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

1998-03-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A novel "self-negotiation framework" has been developed to enable service robots operating in public environments to make ethical decisions during task interruptions caused by simultaneous user requests. This framework allows a single robot to autonomously arbitrate conflicts by representing each user with an "ethical profile" that captures their contextual ethical preferences and conditions. Conflicts are resolved through an internal negotiation process, eliminating the need for external coordination. Implemented in a modular ROS-based architecture, the system was evaluated in simulation using a realistic interruption scenario. Results demonstrate that the framework consistently produces outcomes aligned with user ethical preferences, supports multilateral negotiation among users, and responds within 1.5 seconds, exhibiting near-linear runtime growth even with increasing user input.

Key takeaway

For robotics engineers designing service robots for multi-user public environments, integrating a self-negotiation framework is crucial for ethical autonomy. This approach allows your robots to internally arbitrate conflicting user requests based on individual ethical profiles, ensuring decisions align with human values rather than just efficiency. You should consider implementing similar internal arbitration mechanisms, leveraging modular architectures and transient data management to enhance ethical responsiveness and user trust.

Key insights

Service robots can autonomously resolve multi-user ethical conflicts via internal self-negotiation based on individual ethical profiles.

Principles

• Model user ethical preferences as soft constraints.
• Prioritize internal conflict resolution for autonomy.
• Ensure privacy by transiently storing user data.

Method

The robot internally alternates perspectives, generating partial offers based on one user's status and evaluating them against another's ethical profile using ethical impact scores. This incremental disclosure preserves privacy.

In practice

• Collect user ethical profiles via a dedicated application.
• Utilize ROS 2 lifecycle nodes for transient data handling.
• Quantify ethical impact with numerical scores from dispositions.

Topics

• Service Robots
• Ethical Decision-Making
• Self-Negotiation
• Human-Robot Interaction
• ROS 2 Architecture
• Task Interruptions

Best for: Research Scientist, AI Scientist, Robotics Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.