SimER is an Generative AI-driven simulation environment to assist call-taker training under emergency response scenarios. SimER tailors the most up-to-date LLMs as its backend agents with a sophisticatedly designed prompt generation algorithm to generate realistic, valid, and equitable calls. SimER debriefs the past simulation and offers advice to the user for future improvements. SimER also records feedback from users and iteratively refines the system with the local dispatching center.
Chen, Zirong, Xutong Sun, Yuanhe Li, and Meiyi Ma. “Auto311: A Confidence-Guided Automated System for Non-emergency Calls.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 20, pp. 21967-21975. 2024.
Assistant Professor of Computer Science
School of Engineering
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