用自然语言生成扑克游戏，让普通人也能轻松造游戏。
Instruction-Driven Game Engine: A Poker Case Study
- 把游戏开发转为预测下一步状态的任务，让大模型理解指令生成玩法。
- 能支持多种扑克规则，并通过自然语言创建个性化新游戏。
- 适合想快速原型设计的开发者或教育场景使用。
Instruction-Driven Game Engine (IDGE) 项目旨在通过大语言模型（LLM）理解自由形式的游戏描述并生成游戏流程，降低游戏开发门槛。我们把 IDGE 的学习过程建模为下一状态预测任务，即在给定玩家行为后，模型自回归预测游戏状态。由于游戏状态计算必须精确，微小误差可能导致体验崩坏，因此面临稳定性与多样性之间的矛盾。为此，我们采用渐进式课程训练，逐步提升模型对复杂场景的处理能力。初步成果是构建了支持多种扑克变体的 IDGE，还能通过自然语言输入实现高度个性化的新型扑克游戏。该工作为未来游戏创作与交互方式革新奠定了基础。
原文摘要 · Abstract (English)
The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise; otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played.
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