We realized from our work in oil and gas that the most valuable type of systems to provide decision support were systems that provided the arch from knowledge of symptoms based on real-time situational awareness to optimized actions to address the symptoms. There is a type of machine learning good for that type of optimization called reinforcement learning (aka approximate dynamic programming). In the original TreatSim concept, we used a (power flow) simulation of the electrical grid infrastructure coupled with reinforcement learning to learn how to avoid or mitigate threats to the grid, natural or manmade. TheatSim can be used for various other infrastructures such as water, transportation, pipelines, etc.
Preprints and articles that have been added as the result of this project.List of final papers from the project
Research that was mentioned or added to this project.List of research referenced in this project