用可解释的势场模型让守卫自动巡逻与追捕，更自然且无需训练。
Generic Guard AI in Stealth Game with Composite Potential Fields
- 通过信息、信心、连通三张势场图融合决策，设计可控参数。
- 在五张地图上表现优于传统方法，追捕效率和巡逻自然性双提升。
- 支持干扰物和环境元素模块化集成，适合快速开发新玩法。
守卫巡逻行为是潜行游戏沉浸感与策略深度的核心，但现有系统多依赖手工路径或专用逻辑，在覆盖率、响应追捕能力与行为自然性之间难以平衡。本文提出一种通用、完全可解释、无需训练的框架，通过复合势场整合全局知识与局部信息，将信息、信心、连通三张可解释地图融合为单一核滤决策准则。该参数化、设计师驱动的方法仅需少量衰减与权重参数，无需重训练，即可在占用栅格与NavMesh划分两种抽象下平滑适配。我们在五张代表性游戏地图、两种玩家控制策略及五种守卫模式下进行评估，结果表明，本方法在追捕效率与巡逻自然性上均优于经典基线方法。最后，我们展示常见潜行机制——如干扰物与环境元素——可作为子模块自然融入该框架，实现丰富、动态、响应迅速的守卫行为的快速原型构建。
原文摘要 · Abstract (English)
Guard patrol behavior is central to the immersion and strategic depth of stealth games, while most existing systems rely on hand-crafted routes or specialized logic that struggle to balance coverage efficiency and responsive pursuit with believable naturalness. We propose a generic, fully explainable, training-free framework that integrates global knowledge and local information via Composite Potential Fields, combining three interpretable maps-Information, Confidence, and Connectivity-into a single kernel-filtered decision criterion. Our parametric, designer-driven approach requires only a handful of decay and weight parameters-no retraining-to smoothly adapt across both occupancy-grid and NavMesh-partition abstractions. We evaluate on five representative game maps, two player-control policies, and five guard modes, confirming that our method outperforms classical baseline methods in both capture efficiency and patrol naturalness. Finally, we show how common stealth mechanics-distractions and environmental elements-integrate naturally into our framework as sub modules, enabling rapid prototyping of rich, dynamic, and responsive guard behaviors.
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