用可执行本体让游戏角色行为自动涌现，摆脱繁琐编码。
Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling
- 用声明式规则定义世界状态，行为由条件自然触发。
- 在生存游戏里实现任务优先级中断，无需显式抢占逻辑。
- 适合需要动态响应的开放世界游戏，也利于智能调试。
本文探讨了通过boldsea框架实现的可执行本体（EO）在游戏开发中的应用。我们认为，EO代表了一种范式转变：从算法化的行为编程转向语义世界建模，使代理行为从声明式领域规则中自然涌现，而非显式编码。以生存游戏《冬日盛宴》为例，我们展示了EO如何通过数据流条件实现基于优先级的任务中断，而非依赖显式抢占逻辑。与行为树（BT）和目标导向行动规划（GOAP）相比，这些方法仅描述代理应做什么，而EO则刻画动作何时变得可能——这一根本差异弥补了游戏AI架构中的语义-过程鸿沟。文章还讨论了集成策略、时间事件图带来的天然调试优势，以及大模型驱动运行时模型生成的潜力。
原文摘要 · Abstract (English)
This paper examines the application of Executable Ontologies (EO), implemented through the boldsea framework, to game development. We argue that EO represents a paradigm shift: a transition from algorithmic behavior programming to semantic world modeling, where agent behavior emerges naturally from declarative domain rules rather than being explicitly coded. Using a survival game scenario (Winter Feast), we demonstrate how EO achieves prioritybased task interruption through dataflow conditions rather than explicit preemption logic. Comparison with Behavior Trees (BT) and Goal-Oriented Action Planning (GOAP) reveals that while these approaches model what agents should do, EO models when actions become possible - a fundamental difference that addresses the semantic-process gap in game AI architecture. We discuss integration strategies, debugging advantages inherent to temporal event graphs, and the potential for LLM-driven runtime model generation.
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