We are developing an agent-based model that allows us to explore control and distributed machine learning concepts in a systems-of-systems framework. The model captures the complex interactions between vehicles with very different operational tempos (such as satellites and unmanned aerial vehicles (UAVs)) and a variety of environmental elements (communication towers, objects of interest, etc.). Treating the model as a complex adaptive system, we can explore issues of controllability and observability, such as the constraints needed to maintain multi-vehicle formations under diverse conditions, and scaling questions, such as the data rates among vehicles and control centers under diverse system parameters.

© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Jason Crane, Islam Hussein, Ronnie Mainieri, and Lorraine Weis, "Theoretical development of multi-domain command and control," Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100605 (Presented at SPIE Defense + Commercial Sensing: April 15, 2019; Published: 10 May 2019); https://doi.org/10.1117/12.2518894.