Large language models trained for code generation can be applied to speaking
virtual worlds into existence (creating virtual worlds). In this work we show
that prompt-based methods can both accelerate in-VR level editing, as well as
can become part of gameplay rather than just part of game development. As an
example, we present Codex VR Pong which shows non-deterministic game mechanics
using generative processes to not only create static content but also
non-trivial interactions between 3D objects. This demonstration naturally leads
to an integral discussion on how one would evaluate and benchmark experiences
created by generative models - as there are no qualitative or quantitative
metrics that apply in these scenarios. We conclude by discussing impending
challenges of AI-assisted co-creation in VR.

本文介绍了利用大型语言模型进行代码生成的方法，以实现在虚拟现实世界中运用生成过程、计算机协助创作游戏和制作静态内容，同时探讨了如何评估和基准测试由生成模型创建的交互体验以及 AI 辅助共同创作在虚拟现实中的挑战。

构建虚拟世界：从提示到实现的步骤

Steps towards prompt-based creation of virtual worlds

The process of playtesting a game is subjective, expensive and incomplete. In
this paper, we present a playtesting approach that explores the game space with
automated agents and collects data to answer questions posed by the designers.
Rather than have agents interacting with an actual game client, this approach
recreates the bare bone mechanics of the game as a separate system. Our agent
is able to play in minutes what would take testers days of organic gameplay.
The analysis of thousands of game simulations exposed imbalances in game
actions, identified inconsequential rewards and evaluated the effectiveness of
optional strategic choices. Our test case game, The Sims Mobile, was recently
released and the findings shown here influenced design changes that resulted in
improved player experience.

本文提出了一种使用自动代理进行游戏测试的方法，该方法可以以分钟级的速度完成测试者需要数天才能完成的有机游戏。该测试方法的应用在《模拟人生移动版》中取得了积极的效果，对游戏的平衡性，奖励设置以及选项效果进行了评估和优化，从而提升了玩家体验。