Inference Engines in AI: Forward and Backward Chaining

How do artificial intelligence systems reason through complex problems to make logical decisions? At the core of rule-based AI lie inference engines, which use systematic strategies to derive new facts from existing knowledge. This text-based course guides you through the foundational principles of logical reasoning, helping you understand how modern systems process data to solve real-world problems. You will transition from grasping basic logical rules to designing structured reasoning paths. By exploring both data-driven and goal-driven methodologies, you will gain the clarity needed to choose the right reasoning strategy for any computational challenge. What you'll learn: - Understand the core terminology of knowledge representation, facts, and production rules. - Master forward chaining to drive logical conclusions from a set of known starting data. - Apply backward chaining to prove specific hypotheses by working in reverse from a goal. - Compare the computational trade-offs, efficiency, and appropriate use cases for both methods. - Design structured decision-making logic using modern rule-based programming patterns. - Explore how modern AI architectures combine heuristic search and chaining for hybrid reasoning. This course begins with fundamental definitions of propositional logic and knowledge bases before guiding you through step-by-step reasoning algorithms. You will read through clear, structured pseudocode and practical scenarios that illustrate how expert systems diagnose issues, plan actions, and analyze data. This course is designed for beginners, developers, and aspiring AI engineers who want to understand the logic behind expert systems. No prior background in artificial intelligence or advanced mathematics is required. Start reading today to unlock the power of logical reasoning in artificial intelligence.