Robots Can Feel: LLM-based Framework for Robot Ethical Reasoning
Artem Lykov, Miguel Altamirano Cabrera, Koffivi Fid\`ele Gbagbe and, Dzmitry Tsetserukou

TL;DR
This paper introduces a novel ethical reasoning framework for robots that combines logic and emotion simulation, allowing robots to make morally complex decisions similar to humans, adaptable across various large language models.
Contribution
The paper presents the first system integrating logic and emotion simulation for robot ethical reasoning, independent of the base language model used.
Findings
Emotion Weight Coefficient significantly affects robot decisions.
System tested successfully on 8 different large language models.
Statistical analysis confirms the influence of emotional parameters on decision outcomes.
Abstract
This paper presents the development of a novel ethical reasoning framework for robots. "Robots Can Feel" is the first system for robots that utilizes a combination of logic and human-like emotion simulation to make decisions in morally complex situations akin to humans. The key feature of the approach is the management of the Emotion Weight Coefficient - a customizable parameter to assign the role of emotions in robot decision-making. The system aims to serve as a tool that can equip robots of any form and purpose with ethical behavior close to human standards. Besides the platform, the system is independent of the choice of the base model. During the evaluation, the system was tested on 8 top up-to-date LLMs (Large Language Models). This list included both commercial and open-source models developed by various companies and countries. The research demonstrated that regardless of the…
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Taxonomy
TopicsEthics and Social Impacts of AI · Adversarial Robustness in Machine Learning
