Probabilistic Model-Based Programming Techniques for Prediction, Analysis, and Control (PROMPT)

Harrison, S., Takata, G., Wu, C., and Pfeffer, A.

Presented at the 15th Annual Conference on Systems Engineering Research Disciplinary Convergence: Implications for Systems Engineering Research, Redondo Beach, CA (March 2017)

Model-based systems engineering (MBSE) frameworks such as SysML provide declarative descriptions of the structure, processes, functions, and context of a system. However, these frameworks do not support modeling uncertainties in system design and operating environments that influence a system’s real-world behavior. Charles River Analytics is developing a system of Probabilistic Model-Based Programming Techniques for Prediction, Analysis, and Control (PROMPT). PROMPT provides probabilistic extensions for SysML, enabling representation of uncertainties in both structure and behaviors of complex systems, as well as interactions between structures and behaviors. PROMPT also provides inference services built on the open-source Figaro™ probabilistic programming language, supporting prediction of system performance and analysis of designs in the face of uncertainty. Our initial evaluation show that enhancing MBSE frameworks with probabilistic capabilities can enable accurate analyses and predictions of system performance under uncertainty, enabling system modelers to create better designs faster.

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