IF:CARGO：面向AI原生规则编程游戏的基于大语言模型的语义编译
IF:CARGO: LLM-Based Semantic Compilation for Al-Native Rule Programming Games
AI总结：
本研究通过实验性益智游戏IF:CARGO，将大语言模型用作语义编译器而非游戏代理，开展24人混合方法游戏测试，提出AI原生游戏需约束自然语言输入、保留玩家创作权并确保确定性执行的模式。
AI中文摘要：
本案例研究展示了IF:CARGO，一款实验性益智游戏，它将大语言模型用作语义编译器，而非自主游戏代理。玩家用自然语言编写IF/THEN规则，模型将其转换为受限命令模式，供游戏引擎进行确定性验证与执行。该架构构建了可玩的“表达-执行-观察-修正”循环，将AI交互构建成语义调试。针对8个关卡的24名参与者开展混合方法游戏测试，考察玩家尝试次数、思考时长、感知可控性、可调整性及对AI角色的解读。结果表明，玩家普遍将模型理解为翻译中介，可通过反馈修正策略；而周期性命令、多机器人协调及规则优先级机制带来了更高的认知与诊断需求。本研究提出了AI原生游戏的实用模式：约束自然语言输入、保留玩家创作权、确保确定性执行。
英文摘要：
This case study presents IF: CARGO, an experimental puzzle game that uses a large language model as a semantic compiler rather than an autonomous game-playing agent. Players author IF/THEN rules in natural language, which the model translates into a constrained command schema for deterministic validation and execution by the game engine. This architecture creates a playable loop of expression, execution, observation, and revision, framing AI interaction as semantic debugging. A mixed-methods playtest with 24 participants across eight levels examined player attempts, thinking time, perceived controllability, adjustability, and interpretations of the AI's role. Results suggest that players generally understood the model as a translation intermediary and could revise their strategies through feedback, while periodic commands, multi-robot coordination, and rule-priority mechanics created greater cognitive and diagnostic demands. The study proposes a practical pattern for AI-native gameplay: constrain natural-language input, preserve player authorship, and ensure deterministic execution.
