Creatively translating complex gameplay ideas into executable artifacts (e.g., games as Unity projects and code) remains a central challenge in computational game creativity. Gameplay design patterns provide a structured representation for describing gameplay phenomena, enabling designers to decompose high-level ideas into entities, constraints, and rule-driven dynamics. Among them, goal patterns formalize common player-objective relationships. Goal Playable Concepts (GPCs) operationalize these abstractions as playable Unity engine implementations, supporting experiential exploration and compositional gameplay design. We frame scalable playable pattern realization as a problem of constrained executable creative synthesis: generated artifacts must satisfy Unity's syntactic and architectural requirements while preserving the semantic gameplay meanings encoded in goal patterns. This dual constraint limits scalability. Therefore, we investigate whether contemporary large language models (LLMs) can perform such synthesis under engine-level structural constraints and generate Unity code (as games) structured and conditioned by goal playable patterns. Using 26 goal pattern instantiations, we compare a direct generation baseline (natural language -> C# -> Unity) with pipelines conditioned on a human-authored Unity-specific intermediate representation (IR), across three IR configurations and two open-source models (DeepSeek-Coder-V2-Lite-Instruct and Qwen2.5-Coder-7B-Instruct). Compilation success is evaluated via automated Unity replay. We propose grounding and hygiene failure modes, identifying structural and project-level grounding as primary bottlenecks.

翻译：将复杂的游戏玩法创意创造性地转化为可执行产物（如Unity项目与代码形式的游戏）仍是计算游戏创造力领域的核心挑战。游戏玩法设计模式为描述游戏玩法现象提供了一种结构化表征，使设计者能够将高层级创意分解为实体、约束和规则驱动的动态机制。其中，目标模式将常见的玩家-目标关系形式化。目标可玩概念将这些抽象概念操作化为可玩的Unity引擎实现，支持体验式探索与组合式游戏玩法设计。我们将可扩展的可玩模式实现问题框架化为约束性可执行创意合成问题：生成的产物必须满足Unity的句法与架构要求，同时保留编码于目标模式中的语义游戏玩法含义。这种双重约束限制了可扩展性。因此，我们研究当代大语言模型是否能在引擎层级的结构约束下执行此类合成，并生成由目标可玩模式结构化与条件化的Unity代码（作为游戏）。基于26个目标模式实例化，我们比较了直接生成基线（自然语言 -> C# -> Unity）与基于人工编写的Unity专用中间表示的条件化生成流程，涵盖三种IR配置和两种开源模型（DeepSeek-Coder-V2-Lite-Instruct与Qwen2.5-Coder-7B-Instruct）。通过自动化Unity回放评估编译成功率。我们提出了基础化与洁净度失效模式，指出结构级与项目级的基础化是主要瓶颈。