一句话：提出利用LLM实现自动游戏试玩测试的愿景，并指出视觉感知与游戏状态文本化接口缺失等关键挑战。
游戏试玩测试对游戏软件质量保证至关重要，但人工试玩耗时且昂贵。自动化这一过程十分困难，因为试玩测试通常需要领域知识和问题求解能力，而多数传统测试工具并不具备这些能力。近期人工智能进展为将大语言模型（LLM）用于试玩测试带来了新可能，但仍存在显著挑战：当前LLM无法视觉感知游戏环境；多数已有研究集中于文本游戏或具有完善API的游戏；许多非文本游戏缺少可用API来提供游戏状态的文本描述，使得朴素地应用LLM进行试玩测试几乎不可行。本文旨在推动基于LLM的自动游戏试玩测试，并指出上述开放问题。
Playtest is the process in which people play a video game for testing. It is critical for the quality assurance of gaming software. Manual playtest is time-consuming and expensive. However, automating this process is challenging, as playtest typically requires for the domain knowledge and problem-solving skills that most conventional testing tools lack. Recent advancements in artificial intelligence (AI) have opened up new possibilities of applying Large Language Models (LLMs) to playtest. However, significant challenges remain: current LLMs cannot visually perceive game environments; most existing research focuses on text-based games or games with robust API; While many non-text games lack APIs to provide textual descriptions of game states, making it almost impossible to naïvely apply LLMs for playtest.