Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as well as providing valuable benchmarks and virtual environments to advance AI technologies in general. This report presents a high-level overview of five promising research pathways for applying state-of-the-art AI methods, particularly deep learning, to digital gaming within the context of the current research landscape. The objective of this work is to outline a curated, non-exhaustive list of encouraging research directions at the intersection of AI and video games that may serve to inspire more rigorous and comprehensive research efforts in the future. We discuss (i) investigating large language models as core engines for game agent modelling, (ii) using neural cellular automata for procedural game content generation, (iii) accelerating computationally expensive in-game simulations via deep surrogate modelling, (iv) leveraging self-supervised learning to obtain useful video game state embeddings, and (v) training generative models of interactive worlds using unlabelled video data. We also briefly address current technical challenges associated with the integration of advanced deep learning systems into video game development, and indicate key areas where further progress is likely to be beneficial.

翻译：电子游戏是人工智能系统一个自然而协同的应用领域，它既具有提升玩家体验与沉浸感的潜力，也为推进通用人工智能技术提供了宝贵的基准测试环境和虚拟场景。本报告在当前研究背景下，对将最先进的人工智能方法（特别是深度学习）应用于数字游戏的五条前景广阔的研究路径进行了高层次概述。本工作的目标是勾勒出一份经过筛选但非穷尽的、令人鼓舞的研究方向清单，这些方向位于人工智能与电子游戏的交叉领域，旨在启发未来更严谨和更全面的研究工作。我们讨论了：（i）研究将大型语言模型作为游戏智能体建模的核心引擎，（ii）使用神经元胞自动机进行程序化游戏内容生成，（iii）通过深度代理建模加速计算成本高昂的游戏内模拟，（iv）利用自监督学习获取有用的视频游戏状态嵌入表示，以及（v）使用未标记的视频数据训练交互式世界的生成模型。我们还简要探讨了当前将先进深度学习系统集成到视频游戏开发中所面临的技术挑战，并指出了可能产生有益进展的关键领域。