发表机构
National University of Singapore; Nanyang Technological University（新加坡国立大学; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文梳理基础模型在游戏全生命周期中的六类应用角色，指出跨角色迁移的机遇与挑战，强调需在特定游戏情境中验证有效性。
AI 中文摘要
基础模型，连同学习型游戏世界模型的进展，正在重塑整个游戏生命周期中的人工智能。除了玩游戏之外，最近的系统还对玩家和游戏动态进行建模，支持设计与开发，在运行时调整面向玩家的体验，并评估由此产生的工件。然而，这些方向在很大程度上是分开发展的，这掩盖了哪些能力可以在不同设置之间迁移，以及哪些能力仍然与特定游戏、引擎、界面或玩家群体绑定。我们根据人工智能输出的直接用途，将文献组织为六个角色：游戏与行动；玩家与游戏建模；游戏设计；游戏构建与维护；运行时生成与调整；以及游戏测试与评估。对于每个角色，我们考察游戏或工作流程提供了什么结构，人工智能学习或产生了什么，哪些能力和工件可以在不同设置和角色之间迁移，以及哪些证据支持这些主张。我们识别出跨角色的联系：轨迹训练世界模型，学习环境为智能体提供经验，设计规范驱动可执行的实现，游戏或测试反馈指导修订。然而，控制方案、规则、引擎接口、状态表示和玩家上下文通常仍是特定于设置的，因此下游主张需要在目标设置中进行验证。评估在受限的游戏玩法和选定的学习环境中最标准化，而学习世界中的持久状态、重复的软件修订、经过验证的玩家建模、持续的运行时调整和代表性的自动化测试仍不太成熟。核心挑战是在不同角色之间重用或迁移输出和能力，同时在使用这些输出的特定游戏情境中重新建立有效性的证据。
英文摘要
Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems model players and game dynamics, support design and development, adapt player-facing experiences at runtime, and evaluate resulting artifacts. Yet these directions have evolved largely separately, obscuring which capabilities transfer across settings and which remain tied to particular games, engines, interfaces, or player populations. We organize the literature into six roles according to the immediate use of AI output: playing and acting; modeling players and games; designing games; building and maintaining games; generating and adapting at runtime; and testing and evaluating games. For each role, we examine what structure is supplied by the game or workflow, what AI learns or produces, which capabilities and artifacts transfer across settings and roles, and what evidence supports the claims. We identify cross-role connections: trajectories train world models, learned environments provide experience for agents, design specifications drive executable implementations, and play or testing feedback guides revision. However, control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing and selected learned environments, while persistent state in learned worlds, repeated software revision, validated player modeling, sustained runtime adaptation, and representative automated testing remain less established. The central challenge is to reuse or transfer outputs and capabilities across roles while re-establishing evidence for effectiveness in the game-specific contexts where they are used.
Comments120 pages, 27 figures, 21 tables. Project page: https://eurekaleo.github.io/awesome-ai-for-games
