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

arXiv: https://arxiv.org/abs/2507.22664 · HTML: https://mlsanity.com/paper/2507.22664/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 181 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Implementations in Machine Ethics: A Survey](https://mlsanity.com/paper/2001.07573/) (2020)
- [Landscape of Machine Implemented Ethics](https://mlsanity.com/paper/2009.00335/) (2020)
- [Negotiating Team Formation Using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.10380/) (2020)
- [On Specifying for Trustworthiness](https://mlsanity.com/paper/2206.11421/) (2022)
- [Logic programming for deliberative robotic task planning](https://mlsanity.com/paper/2301.07550/) (2023)
- [Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots](https://mlsanity.com/paper/1903.03920/) (2019)
- [FogROS: An Adaptive Framework for Automating Fog Robotics Deployment](https://mlsanity.com/paper/2108.11355/) (2021)
- [Operationalizing Human Values in Software Engineering: A Survey](https://mlsanity.com/paper/2108.05624/) (2021)
- [Specification, Validation and Verification of Social, Legal, Ethical, Empathetic and Cultural Requirements for Autonomous Agents](https://mlsanity.com/paper/2307.03697/) (2023)
- [Ethics in the Age of AI: An Analysis of AI Practitioners' Awareness and Challenges](https://mlsanity.com/paper/2307.10057/) (2023)
- [Analyzing and Debugging Normative Requirements via Satisfiability Checking](https://mlsanity.com/paper/2401.05673/) (2024)
- [Normative Requirements Operationalization with Large Language Models](https://mlsanity.com/paper/2404.12335/) (2024)
- [The Jiminy Advisor: Moral Agreements Among Stakeholders Based on Norms and Argumentation](https://mlsanity.com/paper/1812.04741/) (2018)
- [A Study on the Prevalence of Human Values in Software Engineering Publications, 2015-2018](https://mlsanity.com/paper/1907.07874/) (2019)
- [Avatar Work: Telework for Disabled People Unable to Go Outside by Using Avatar Robots "OriHime-D" and Its Verification](https://mlsanity.com/paper/2003.12569/) (2020)
- [Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation](https://mlsanity.com/paper/2201.02455/) (2022)
- [Exosoul: ethical profiling in the digital world](https://mlsanity.com/paper/2204.01588/) (2022)
- [Modelling resource contention in multi-robot task allocation problems with uncertain timing](https://mlsanity.com/paper/1607.04358/) (2016)
- [An Ontology-based Context Model in Intelligent Environments](https://mlsanity.com/paper/2003.05055/) (2020)
- [Leveraging Privacy Profiles to Empower Users in the Digital Society](https://mlsanity.com/paper/2204.00011/) (2022)
- [On Representing Humans' Soft-Ethics Preferences As Dispositions](https://mlsanity.com/paper/2408.06355/) (2024)
- [Engineering Digital Systems for Humanity: a Research Roadmap](https://mlsanity.com/paper/2412.19668/) (2024)
- [Engineering Human Values in Software through Value Programming](https://mlsanity.com/paper/2003.04477/) (2020)

## Abstract (arXiv)

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.