Cognitive Computing Intelligence to Assist Avionics Analytics

Safety aviation assessment is an increasing concern in Air and Unmanned Aerial System Traffic Management (ATM/UTM). For ATM/UTM situations, there is a need to investigate methodologies and tools to assist air personnel in making decisions. This paper discusses a cognitive model for a Decision-Support System (DSS) to facilitate ATM/UTM. The knowledge-intensive modeling is meant to be the foundation of the comprehension needed for situation awareness about the surroundings of the operation context of the DSS. This paper presents results from dependability verification (logical consistency) of an Avionics Analytics Ontology (AAO). The verification process involves airspace situations including weather conditions and drones. Concluding remarks and further steps for this research work are also discussed.