Abstract

Active shooter incidents are posing an increasing threat to public safety. Given the majority of the past incidents took place in built environments (e.g., educational, commercial buildings), there is an urgent need for a method to assess the safety of buildings under an active shooter situation. This study aims to bridge this knowledge gap by developing a learning technique that can be used to model the behavior of the shooter and the trapped civilians under an active shooter incident. Understanding how the civilians respond to different simulated environments, a number of actions can be undertaken to bolster the safety measures of a given facility. This study provides a customized decision-making tool that adopts a tailored maximum entropy inverse reinforcement learning algorithm and utilizes some safety measurement metrics, such as the percentage of civilians who can hide/exit in/from the system, to assess a workplace’s safety under an active shooter incident. For instance, our results demonstrate how different building configurations (e.g., location and number of entrances/exits, hiding places) play a significant role in the safety of civilians under an active shooter situation. The results further demonstrate that the shooter’s prior shooting experiences, the type of firearm carried, and the timing of the incident are some of the important factors that may pose serious security concerns to the civilians under an active shooter incident.

摘要

活跃的射手事件对公共安全构成越来越大的威胁。鉴于过去大多数事件发生在建筑环境（例如，教育、商业建筑）中，迫切需要一种方法来评估主动射击情况下建筑物的安全性。本研究旨在通过开发一种学习技术来弥合这一知识差距，该技术可用于模拟射手和被困平民在主动射手事件下的行为。了解平民如何应对不同的模拟环境，可以采取许多行动来加强特定设施的安全措施。本研究提供了一种定制的决策工具，采用量身定制的最大熵逆强化学习算法，并利用一些安全测量指标，例如可以隐藏/退出系统的平民百分比，以评估活跃射手事件下工作场所的安全性。例如，我们的结果证明了不同的建筑配置（例如，入口/出口的位置和数量、藏身之处）如何在活跃的射手情况下对平民的安全发挥重要作用。结果进一步表明，射手之前的射击经历、携带的枪支类型和事件发生的时间是一些重要因素，可能会对活跃射手事件中的平民造成严重的安全问题。出入口的位置和数量、藏身之处）在活跃的射手情况下对平民的安全起着重要作用。结果进一步表明，射手之前的射击经历、携带的枪支类型和事件发生的时间是一些重要因素，可能会对活跃射手事件中的平民造成严重的安全问题。出入口的位置和数量、藏身之处）在活跃的射手情况下对平民的安全起着重要作用。结果进一步表明，射手之前的射击经历、携带的枪支类型和事件发生的时间是一些重要因素，可能会对活跃射手事件中的平民造成严重的安全问题。