Principles of Autonomy and Decision-Making Algorithms

How do autonomous systems, from smart robotic pathfinders to automated decision-support software, make complex choices without human intervention? Understanding the underlying computational models is key to designing intelligent systems that can reason, optimize, and plan under uncertainty. This text-based course provides a comprehensive introduction to the algorithms and mathematical models behind autonomous decision-making. You will learn how to translate complex real-world challenges into structured problems that can be solved using artificial intelligence and operations research techniques. What you'll learn: Understand core reasoning paradigms, including propositional logic, deduction, and constraint-based search; Apply heuristic search algorithms to navigate complex decision spaces efficiently; Formulate and solve optimization problems using linear, integer, and dynamic programming; Master decision-theoretic planning and model uncertainty using Markov Decision Processes (MDPs); Explore modern autonomous execution frameworks, including basic reinforcement learning concepts and planning architectures. The journey begins with foundational logic and classical search strategies before moving into optimization techniques and probabilistic decision-making under uncertainty. Through clear written explanations and structured algorithmic walk-throughs, you will build a solid theoretical and practical foundation. Designed for beginners in computer science, robotics, or engineering who want to understand autonomous decision systems, this course requires no advanced prior background in AI. Start reading to master the algorithmic principles that power modern autonomous systems.