用VR数据训练模拟枪手行为的仿真系统，可高效测试安防策略。
Developing a Discrete-Event Simulator of School Shooter Behavior from VR Data
- 基于VR实验数据建模枪手行动与移动的随机过程。
- 仿真系统复现真实行为模式，支持大规模策略测试。
- 适合研究智能安防机器人等自主干预方案的团队。
虚拟现实（VR）已成为评估校园安全措施在高风险场景（如校园枪击）中有效性的有力工具，具备实验控制和高行为保真度的优势。然而，在VR中评估新干预措施需为每种条件招募新受试者，导致大规模或迭代评估困难。这一限制在学习有效干预策略时尤为突出，因策略通常需大量训练回合。为此，我们开发了一种基于数据的离散事件仿真器（DES），将枪手移动与区域内的行为建模为从参与者VR实验中学习到的随机过程。利用该仿真器，我们检验了基于机器人的枪手干预策略的效果。经验证，该仿真器能复现关键的实证行为模式，从而实现不可直接通过人类受试者训练的干预策略的可扩展评估与学习。总体而言，本工作展示了一个高至中等保真度的仿真流程，为开发和评估自主校园安全干预措施提供了可扩展的替代方案。
原文摘要 · Abstract (English)
Virtual reality (VR) has emerged as a powerful tool for evaluating school security measures in high-risk scenarios such as school shootings, offering experimental control and high behavioral fidelity. However, assessing new interventions in VR requires recruiting new participant cohorts for each condition, making large-scale or iterative evaluation difficult. These limitations are especially restrictive when attempting to learn effective intervention strategies, which typically require many training episodes. To address this challenge, we develop a data-driven discrete-event simulator (DES) that models shooter movement and in-region actions as stochastic processes learned from participant behavior in VR studies. We use the simulator to examine the impact of a robot-based shooter intervention strategy. Once shown to reproduce key empirical patterns, the DES enables scalable evaluation and learning of intervention strategies that are infeasible to train directly with human subjects. Overall, this work demonstrates a high-to-mid fidelity simulation workflow that provides a scalable surrogate for developing and evaluating autonomous school-security interventions.
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