NATO Lecture Series Set-290

Theme:

Digitalization in the defense domain enables military decision-makers to consciously perceive and responsibly act even in the highly complex and accelerated technosphere of modern conflicts. If applied to Military Multiple Sensor Fusion Engines, the methods of Artificial Intelligence (AI), a world of algorithms that comprises much more than just Machine Learning, combined with comprehensive automation become game changers by transforming vast data streams from many sources into situation pictures by an optimized use of available sensing, communications, and platform resources.

For this reason, there is an ever increasing need to provide advanced tutorials on the strengths and weaknesses, opportunities and threats of AI-based perception and decision making in the military domain. The targeted audience of the lecture series are systems engineers, project managers, sponsors, military end-users that have to define requirements. Its main objective is to disseminate the existing knowledge on sophisticated AI algorithms, the very driving forces of advanced Multiple Sensor Fusion Engines. An equally important objective is to encourage further R&D on AI in Defense that is focused on the need of NATO’s future missions.

Topics to be covered:

In a tutorial fashion, the lecturers will present methodologies and proven AI algorithms that solve various problems in military situational awareness and decision making under uncertainty. Besides data from multiple heterogeneous sensors, the team will discuss the exploitation of context information for designing Cognitive Fusion Engines that inherently respond to changing scenario and mission requirements. Multifunctionality will be a predominant factor to achieve specialized goals. Emphasis will also be placed on data integrity aspects. This comprises selected and unclassified Electronic Warfare issues. Advanced AI methods and examples taken from probabilistic reasoning, statistical decision making, big/tall/sparse data fusion for tracking, classification, anomaly detection, Bayesian and machine learning, explainable AI, knowledge-representation, multiple hypothesis and logical analysis, sensor and resources management, examples from military applications.