Foundations of AI Knowledge Representation and Reasoning

Building truly intelligent systems requires more than just processing raw data; it requires representing human knowledge in a way that machines can logically interpret and act upon. This text-only course introduces you to the core principles of Knowledge Representation and Reasoning (KRR), the foundational backbone of symbolic artificial intelligence. You will transition from understanding basic computational logic to designing structured representation systems. Through clear written explanations, step-by-step logic derivations, and practical conceptual exercises, you will learn how computers store facts, infer new information, and make decisions using formal reasoning frameworks. What you'll learn: - Understand the fundamental principles of symbolic AI and the role of knowledge representation. - Apply propositional and first-order logic to formalize real-world facts and rules. - Explore structural representation methods, including semantic networks, frames, and inheritance hierarchies. - Master classical reasoning techniques like resolution, unification, and forward/backward chaining. - Discover modern applications of knowledge representation, including ontologies and knowledge graphs. - Analyze how symbolic reasoning integrates with modern AI architectures, such as Retrieval-Augmented Generation (RAG). The course starts with essential definitions and the history of symbolic AI, moving step-by-step through formal logic systems and deduction algorithms. You will then explore structured representation formats and conclude by examining how these classical reasoning methods power modern semantic web technologies and hybrid AI systems. This course is designed for aspiring AI engineers, computer science students, and tech enthusiasts who want to understand the logical rules behind machine intelligence. No prior background in artificial intelligence or advanced mathematics is required. Start reading today to unlock the logical foundations of artificial intelligence.