Understand how autonomous AI systems combine intelligent control, responsible governance, and safety assurance to operate reliably in dynamic environments.
Designed for AI engineers, robotics professionals, control systems specialists, and AI governance practitioners, this course introduces adaptive and optimal control, reactive mechanisms, and sensor fusion. Explore how deliberative, hybrid, cognitive, and behavior-based architectures connect perception, planning, and execution. Examine decentralized multi-agent coordination alongside AI alignment, runtime safety, ethical decision-making, and bias mitigation. Learn how explainable AI, audit trails, trust calibration, and human override mechanisms support transparent, accountable autonomous decisions. You'll also evaluate verification methods and safety assurance strategies for complex autonomous systems. By the end, you'll be able to analyze control architectures, evaluate multi-agent systems, apply responsible AI governance principles, and assess approaches to safety, explainability, and verification. What makes this course distinctive is its combined focus on control architecture, multi-agent governance, and autonomous system assurance—helping you connect technical design decisions with accountability and trust.
