Episode 7 — Search and Problem Solving in AI
Before machine learning took center stage, AI was already grappling with how to solve problems systematically. This episode dives into search and problem solving, two of the earliest and still fundamental approaches to intelligence. You’ll learn how problems are represented as states and transitions, and how uninformed search strategies like breadth-first and depth-first explore possibilities blindly. We’ll then move to informed searches, where heuristics act as shortcuts, guiding algorithms like A* to efficient solutions.
Beyond simple puzzles, we show how these methods apply in real-world settings. Constraint satisfaction problems, optimization tasks, and adversarial search in games demonstrate the versatility of these approaches. We also look at evolutionary algorithms and local search strategies that mimic biological or incremental processes. Applications in robotics, operations research, and planning illustrate why search remains central even in today’s AI. By the end, you’ll recognize search not as a relic, but as a foundation underpinning many techniques you’ll see throughout this course. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.
