Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning
Ajay Vishwanath, Einar Duenger B{\o}hn, Ole-Christoffer Granmo, Charl, Maree, Christian Omlin

TL;DR
This paper explores how role-playing games with moral dilemmas can be used to develop and train artificial agents embodying virtue ethics, leveraging modern AI techniques like reinforcement learning and explainability.
Contribution
It introduces a novel approach to formalize and implement virtue ethics in AI agents through game-based training and ethical decision analysis.
Findings
Proposes a systemic role-playing game framework for virtue development in AI
Utilizes affinity-based reinforcement learning for virtuous decision-making
Incorporates explainable AI to analyze ethical choices
Abstract
Machine ethics has received increasing attention over the past few years because of the need to ensure safe and reliable artificial intelligence (AI). The two dominantly used theories in machine ethics are deontological and utilitarian ethics. Virtue ethics, on the other hand, has often been mentioned as an alternative ethical theory. While this interesting approach has certain advantages over popular ethical theories, little effort has been put into engineering artificial virtuous agents due to challenges in their formalization, codifiability, and the resolution of ethical dilemmas to train virtuous agents. We propose to bridge this gap by using role-playing games riddled with moral dilemmas. There are several such games in existence, such as Papers, Please and Life is Strange, where the main character encounters situations where they must choose the right course of action by giving up…
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Taxonomy
TopicsEthics and Social Impacts of AI · Adversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI)
