C. Cockrell, S. Christley and G. An
University of Vermont, Vermont, United States
Poster stand number: W106
Keywords: multiscale modeling, complex systems, simulation, artificial intelligenceNational security threats arise from complex interactions from complex systems, and it can be extremely difficult to integrate existing knowledge in a fashion that allows examination of multiple scenarios, including counterfactual assessment. High resolution simulation models can aid in these tasks, but there are operational challenges in developing, sustaining and updating such simulations. The Machine Assisted Generation, Calibration and Control (MAGCC) framework provides machine assistance for crucial steps in the development, implementation, testing, and use of scientific simulation models. MAGCC bridges systems for knowledge extraction via natural language processing and existing simulation models to generate new simulation models aided by artificial intelligence (AI). MAGCC includes: 1) the development of a formal knowledge representation knowledgebase, the Structured Scientific Knowledge Representation (SSKR) that encompasses all the types of information needed to make any simulation model, 2) the use of an AI, the Computational Modeling Assistant (CMA), that takes information from the SSKR and generates model specifications across a range of simulation modeling methods, and 3) the use of the CMA to generate code for a simulation model from those model specifications. The MAGCC framework can be customized for any complex system, such as biothreat/pandemic readiness and geopolitical analysis/strategy.