Many current AI and machine learning algorithms and data and information fusion processes attempt in software to estimate situations in our complex world of nested feedback loops. Such algorithms and processes must gracefully and efficiently adapt to technical challenges such as

Introduction: Motivations for and Initiatives on AI Engineering.- Architecting Information Acquisition To Satisfy Competing Goals.- Trusted Entropy-Based Information Maneuverability for AI Information Systems Engineering.- BioSecure Digital Twin: Manufacturing Innovation and Cybersecurity Resilience.- Finding the path toward design of synergistic humancentric complex systems.- Agent Team Action, Brownian Motion and Gambler's Ruin.- How Deep Learning Model Architecture and Software Stack Impacts Training Performance in the Cloud.- How Interdependence Explains the World of Teamwork.- Designing Interactive Machine Learning Systems for GIS Applications.- Faithful Post-hoc Explanation of Recommendation using Optimally Selected Features.- Risk Reduction for Autonomous Systems.- Agile Systems Engineering in Building Complex AI Systems.- Platforms for Assessing Relationships: Trust with Near Ecologically-valid Risk, and Team Interaction.- Principles for AI-Assisted Attention Aware Systems in Human-in-the-loo[p Safety Critical Applications.- Interdependence and vulnerability in systems: A review of theory for autonomous human-machine teams.- Principles of a Accurate Decision and Sense-Making for Virtual Minds.