AI Fundamentals: Adversarial Search, Probabilistic Models & Logical Agents

AI Fundamentals: Adversarial Search, Probabilistic Models & Logical Agents

This topic introduces three core pillars of classical AI: adversarial search for competitive decision-making (Minimax, alpha-beta pruning, k-ply lookahead), probabilistic reasoning with Bayes networks, and logical agents…

• Adversarial search applies when two agents have opposing goals in a zero-sum setting.
• Tic-tac-toe is the canonical testbed because its full game tree is small enough to search exhaustively.
• Minimax assigns utility $+1$, $-1$, $0$ to win, loss, draw and backs values up the tree.

~15 min · full explanation, examples & memory tricks in the app