Artificial Intelligence / Autonomous Systems and Human Autonomy Teaming

Presented By Dr. Steven D. Harbour


The course is designed to appeal to scientific and engineering professionals who wish to obtain and or increase knowledge in Artificial Intelligence / Autonomous Systems and Human Autonomy Teaming. Introduction to the main foundational concepts and techniques used in Artificial Intelligence (AI); including decision making, planning, machine learning, and cognition. Includes a range of real-world applications in which AI is currently used in aeronautical and aerospace systems. Presentation of theoretical concepts occurs. Systematic study of methods and research findings in the field of human perception, with an evaluation of theoretical interpretations. Provides a basis for the understanding of these perceptual capabilities as components in Artificial Intelligence in aviation/aerospace systems. The field of human-autonomy teaming (HAT) is fast becoming a significant area of research, especially in aviation. HAT is highly interdisciplinary, bringing together methodologies and techniques from robotics, artificial intelligence, human-computer interaction, cognitive psychology, neuroscience, neuroergonomics, and other fields. The topics covered will include technologies that enable human-machine interactions, the psychology of interaction between people and machines, how to design and conduct HAT studies, and real-world applications such as assistive machines. Covered are the advanced systematic study of methods and research findings in the field of human and computer perception, with an evaluation of theoretical interpretations. Algorithmic foundations of AI / ML. Additionally, introduction to Autonomous Systems will be covered. Surveys the fundamentals of autonomous aircraft system operations, from sensors, controls, and automation to safety procedures, human factors. Presentation of advanced theoretical concepts for artificial intelligence in the areas of knowledge representation and search techniques. The concept of the perceptron and neuron will be covered along with 1st, 2nd, and 3rd generation neural networks. Machine Learning is also covered: hands-on, live and in-action machine learning problems will be solved: utilizing regression analysis, ANNs, RNNs, CNNs (Deep Learning), SNNs, RELs, SVMs, and Bayesian Belief Networks. This course presents the latest major commercial uses of UAS, and manned aircraft that will be going from 2-pilot operations to 1-pilot operations to unmanned operations.

Dr. Steven D. Harbour, PhD. Principal Engineer & Scientist, Dayton Engineering Advanced Projects Lab, Avionics Division, SwRI. SME in Artificial Intelligence / Machine Learning, Human Autonomy Teaming, Neuroscience, Electrical & Computer Engineering, Avionics, UAS and Autonomous vehicles. A senior leader, defense research & engineering professional with over 25 years of experience in multiple engineering and aviation disciplines & applications. Leads and performs ongoing basic and applied research projects, including the development of third-generation spiking neural networks (SNNs) and neuromorphic applications to include Human Autonomy Teaming. He has supported the Air Force Research Laboratory Sensors Directorate at Wright-Patterson Air Force Base, Ohio, and at the Air Force Life Cycle Management Center in the ISR / SOF directorate as the Global Hawk Chief of Avionics Engineering and Modernization Programs. USAF test pilot with over 5,000 hours total flying time in F-16, F-4, AT-38, T-37, B-52, and EC- 135 aircraft. Flew the MIG-29 as part of the US State Department’s military to military visit program under the Nunn-Lugar Act. PhD in Neuroscience (Specializations: Artificial Intelligence & Machine Learning and Neuroergonomics), MS in Aerospace Engineering & Mathematics (Specializations: Avionics, Controls & Displays), BS in Electrical & Computer Engineering (Specializations: Robotics & Feedback Control Systems and Cognition). Dr. Harbour also teaches at the University of Dayton & Sinclair College.