Virtual crowds for non-combative environments play an important role in modern military operations and often create complications for the combatant forces involved. To address this problem, we are developing crowd simulation capable of generating crowds of non-combative civilians that exhibit a variety of individual and group behaviors at a different level of fidelity. Commercial game technology is used for creating an experimental setup to model an urban megacity environment and the physical behaviors of human characters that make up the crowd. The main objective of this work is to verify the feasibility of designing a collaborative virtual environment (CVE) and its usability for training security agents to respond to emergency situations like active shooter events, bomb blasts, fire and smoke. We present a hybrid (human-artificial) platform where experiments for disaster response can be performed in CVE by including AI agents and User-controlled agents. AI agents are computer controlled agents to include behaviors such as hostile agents, non-hostile agents, leader following agents, goal following agents, selfish agents, and fuzzy agents. User-controlled agents are autonomous agents for specific situation roles such as police officer, medic, firefighter, and swat official. The novelty of our work lies in modeling behaviors for AI agents or computer-controlled agents so that they can interact with user-controlled agents in an immersive training environment for emergency response and decision making. The hybrid platform aids in creating an experimental setup to study human behavior in a megacity for emergency response, decision-making strategies, and what-if scenarios.
Sharad Sharma, Phillip Devreaux, Swetha Sree, David Scribner, Jock Grynovicki, Peter Grazaitis, "Artificial intelligence agents for crowd simulation in an immersive environment for emergency response" in Proc. IS&T Int’l. Symp. on Electronic Imaging: The Engineering Reality of Virtual Reality, 2019, pp 176-1 - 176-8, https://doi.org/10.2352/ISSN.2470-1173.2019.2.ERVR-176