Role of AI-ML in Systems Engineering to Support Human Decision Making

Commercial and military systems have evolved with respect to complexity to incorporate advanced and diverse technologies. Associated with these systems are emerging behaviors that require decision support well beyond the capacity of human reasoning alone. To fill this gap, Artificial Intelligence and Machine Learning (AI/ML) can assist Systems Engineering (SE) with respect to operationally realizing the full potential (e.g., speed, scale, and accuracy) of the capabilities offered by these systems.

This presentation provides a review of the concepts of complex systems and emergent behavior, discusses the fundamentals of AI/ML, and then ties these together to demonstrate the role of AI/ML in systems through specific commercial and military use cases. These use cases include:

1. Object Recognition and Detection Enhancement via Reinforcement Learning Yield (ORDERLY). ORDERLY is an AI/ML-based system that autonomously screens massive collections of sensor data from systems and transforms these raw data into actionable information.

2. Disaggregated Distributed AI Chat Enabler (D2ACE) System. D2ACE applies AI/ML techniques to Chat-based systems. D2CRaB corrects spelling, typos, and other corruption in chat messages, recognizes uncommon language formats, and autonomously prioritizes and reduces the quantity of chat messages to only those relevant to specific objectives and intent for a given mission.

3. Distributed Disaggregated Communications via Reinforcement Learning and Backpressure (D2CRaB): D2CRaB is an AL/ML-based system that introduces two new advances to address problem of effectively communicating within distributed and disaggregated operational environments. These new techniques resolve the immediate congestion issue and then assist with maintaining congestion free network traffic.

In summary, through this background information and these use cases, the audience will emerge from this presentation with a focused understanding of AI’s role in Systems Engineering with respect to supporting human decision-making for complex, emergent Systems.