Foundations of Logic and Expert Systems in AI

Artificial intelligence is built on a foundation of structured reasoning and symbolic logic. To truly understand how modern AI systems make decisions, you must first master the classic rule-based models that paved the way for automated problem-solving. This text-based course guides you through the fundamental principles of logic and expert systems, showing you how to translate human expertise into computer-executable rules. You will start by learning how to represent knowledge systematically using propositional and predicate logic, before moving on to design inference engines that can draw logical conclusions from complex data. By reading through practical scenarios, you will understand how to construct and evaluate rule-based systems from scratch. What you'll learn: - Understand the core principles of propositional and first-order predicate logic in AI - Design structured knowledge bases using clear IF-THEN rules and facts - Implement forward and backward chaining inference techniques to solve problems - Evaluate how expert systems manage uncertainty with confidence factors - Analyze modern hybrid approaches that combine rule-based logic with machine learning - Practice modeling real-world decision workflows as executable rule sets This course begins with essential terminology and the historical foundations of symbolic AI, gradually advancing to the mechanics of inference engines and modern hybrid systems. It is designed for beginners, developers, and aspiring AI engineers who want to understand the logical underpinnings of automated decision-making without needing any prior background in artificial intelligence. Start reading today to master the logical core of AI.