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

TL;DR

RobEthiChor introduces an ethics-based negotiation framework for autonomous robots, enabling personalized decision-making aligned with user moral preferences, demonstrated through real robot experiments with high success rates.

Contribution

This work presents a novel, domain-agnostic architecture and ROS implementation for ethics-based negotiation in autonomous systems, addressing personalization and multi-agent ethical consensus.

Findings

RobEthiChor achieved over 73% agreement in robot negotiations.

Average negotiation time was 0.67 seconds.

The approach is scalable to multiple robots and scenarios.

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.