This project seeks to understand how individuals behave under attack in public spaces to support the development of tailored training and response strategies. Researchers intend to observe and analyze human behavior during active shooter scenarios and leverage the data to develop an inverse reinforcement learning algorithm. The algorithm aims to accurately simulate human responses in varying circumstances to enable the development of more effective attack mitigation strategies for civilians and law enforcement officers.