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
something else dear to them. We draw inspiration from such games to show how a
systemic role-playing game can be designed to develop virtues within an
artificial agent. Using modern day AI techniques, such as affinity-based
reinforcement learning and explainable AI, we motivate the implementation of
virtuous agents that play such role-playing games, and the examination of their
decisions through a virtue ethical lens. The development of such agents and
environments is a first step towards practically formalizing and demonstrating
the value of virtue ethics in the development of ethical agents.

通过使用角色扮演游戏，如 Papers, Please 和 Life is Strange，我们提出设计一款系统性的角色扮演游戏，以培养人工智能中的道德美德，在现代人工智能技术的帮助下，我们激励实现这样的目标，并通过德性伦理学的视角检验他们的决策。

走向人工美德智能体：游戏、困境与机器学习

Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning

Role-playing games (RPGs) have a considerable amount of text in video game
dialogues. Quite often this text is semi-annotated by the game developers. In
this paper, we extract a multilingual dataset of persuasive dialogue from
several RPGs. We show the viability of this data in building a persuasion
detection system using a natural language processing (NLP) model called BERT.
We believe that video games have a lot of unused potential as a datasource for
a variety of NLP tasks. The code and data described in this paper are available
on Zenodo.

本研究提取多语言角色扮演游戏中的劝说对话数据集，并运用自然语言处理模型 BERT 建立劝说检测系统，证明了视频游戏在 NLP 领域中作为数据源的潜力，代码和数据集可在 Zenodo 上获得。

多语言说服检测：视频游戏是 NLP 宝贵的数据源

Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP

With the rapid development of Role-Playing Games (RPGs), players are now
allowed to edit the facial appearance of their in-game characters with their
preferences rather than using default templates. This paper proposes a game
character auto-creation framework that generates in-game characters according
to a player's input face photo. Different from the previous methods that are
designed based on neural style transfer or monocular 3D face reconstruction, we
re-formulate the character auto-creation process in a different point of view:
by predicting a large set of physically meaningful facial parameters under a
self-supervised learning paradigm. Instead of updating facial parameters
iteratively at the input end of the renderer as suggested by previous methods,
which are time-consuming, we introduce a facial parameter translator so that
the creation can be done efficiently through a single forward propagation from
the face embeddings to parameters, with a considerable 1000x computational
speedup. Despite its high efficiency, the interactivity is preserved in our
method where users are allowed to optionally fine-tune the facial parameters on
our creation according to their needs. Our approach also shows better
robustness than previous methods, especially for those photos with head-pose
variance. Comparison results and ablation analysis on seven public face
verification datasets suggest the effectiveness of our method.

本研究提出一种游戏角色自动生成框架，根据玩家的输入面部照片生成游戏角色。通过自我监督学习范式下预测一大组具有物理意义的面部参数，提高了效率，并保留了互动性，在七个公共面部验证数据集上显示出更好的鲁棒性，特别是对于具有头部姿势变化的照片。