Foundations of Artificial Intelligence and State Space Search

Artificial intelligence is transforming industries from finance to healthcare, but understanding how these systems actually solve problems is the key to building them. This written course demystifies the core mechanics of AI, focusing on how agents navigate complex environments to find optimal solutions. By the end of this course, you will transition from a curious observer to a practitioner who understands how AI agents represent problems, search through possibilities, and make rational decisions using foundational search algorithms. What you'll learn: - Understand the fundamental terminology, history, and core components of artificial intelligence. - Represent real-world problems as formal state spaces with initial states, transitions, and goals. - Apply uninformed search strategies like breadth-first and depth-first search to find paths. - Implement informed search techniques, including greedy best-first and A* search, using heuristics. - Analyze how state space search powers modern applications like autonomous navigation and game-playing agents. - Evaluate the computational complexity and efficiency of different search algorithms. This course begins with essential AI terminology and foundational definitions before moving into the step-by-step mechanics of state space representation. You will then read through clear, structured explanations of search algorithms, complete with conceptual walkthroughs and practical code snippets. This course is designed for absolute beginners who want to understand the algorithmic backbone of AI without any prior background in machine learning. Start reading today to master the fundamental search algorithms that power intelligent decision-making systems.