Knowledge Representation and Reasoning for Modern AI

To build truly intelligent systems, AI must do more than predict the next word—it must understand, organize, and reason with logical facts. This text-based course guides you from the absolute basics of symbolic AI to structuring complex domain knowledge that machines can process. You will transition from a beginner to a confident practitioner capable of designing formal ontologies and implementing logical reasoning engines. By understanding how to represent human knowledge systematically, you will unlock new ways to improve decision-making algorithms and enhance modern AI architectures. What you'll learn: - Understand foundational logic, propositional calculus, and first-order predicate logic. - Design structured ontologies and semantic networks to model complex real-world domains. - Apply automated reasoning techniques, including resolution and description logics, to derive new facts. - Create structured knowledge bases using modern standards like RDF and OWL. - Integrate symbolic knowledge representation with modern retrieval-augmented generation (RAG) and large language models. - Practice modeling scenarios through step-by-step written exercises and logical proofs. The journey begins with essential terminology and the core mathematical foundations of logic. From there, you will progress through semantic web technologies and practical ontology engineering, concluding with how symbolic reasoning connects to modern generative AI. This course is designed for aspiring AI engineers, software developers, and tech enthusiasts who want to understand the logical side of artificial intelligence. No prior experience with formal logic or advanced AI is required. Start reading today to master the logical backbone of intelligent systems.