Reasoning has always been at the heart of intelligence, and in this episode we focus on how AI systems use logic to derive conclusions. Starting with propositional and predicate logic, we’ll explain how knowledge can be structured into true or false statements and rules. Deductive, inductive, and abductive reasoning are compared as different ways to reach conclusions from data or hypotheses. You’ll also learn about inference engines and the difference between forward and backward chaining.

We’ll also look at probabilistic reasoning, fuzzy logic, and non-monotonic systems that handle uncertainty and incomplete knowledge. Case studies from medical diagnosis, legal analysis, and robotics planning show reasoning systems at work in practice. Finally, we discuss both the strengths and limitations of logic: it provides clarity and interpretability, but struggles with scale and adaptability. Understanding reasoning is key to seeing how early AI evolved and why hybrid models combining logic with learning are so important today. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.