Architecture for Machine Learning Techniques to Enable Augmented Cognition in the Context of Decision Support Systems

For a wide range of applications, one of the key challenges is to identify an architecture that is suitable for machine learning techniques to enable important augmented cognition capabilities in the context of complex decision support systems. This overview paper presents an architecture framework. The elements of the architecture are described starting with data formatting, a machine learning algorithm taxonomy, components of courses of action, resource management, and finally the role of augmented cognition within the architecture. The paper includes one cyber security example where the architecture framework is employed. The paper concludes with future work in the development of a recommender system.