Knowledge Representation and Expert Systems Fundamentals

Knowledge representation forms the backbone of intelligent decision-making systems and modern artificial intelligence. This text-based course guides you through structured models, inference engines, and rule-based architectures used to build expert systems. You will learn how domain knowledge is captured, structured, and processed to solve complex problems automatically. You will also explore modern advancements in semantic networks, ontologies, and graph-based knowledge integration. What you'll learn: Understand essential terminology and foundational models of knowledge representation; Explore semantic networks, frames, and formal logic structures; Analyze rule-based inference engines and expert system architectures; Apply decision-making algorithms to structured domain knowledge; Examine modern trends including ontologies and knowledge graph concepts. The course begins with core terminology and foundational theories of artificial intelligence before walking through practical rule design and modern decision-support paradigms. Designed for beginners and aspiring tech professionals, no prior background in artificial intelligence is required. Start reading today to build a strong foundation in knowledge-based systems.