Companion notes for the two ACI textbooks: Russell & Norvig — Artificial Intelligence: A Modern Approach, 4th edition (the primary text) and Engelbrecht — Computational Intelligence: An Introduction, 2nd edition (the swarm and evolutionary half). Every chapter is rewritten in plain easy language with a one-minute summary, diagrams, worked examples, gotchas, FAQs, takeaways, and a concept cheatsheet at the end of every chapter you can use as a quick-reference card.
What AI really is, the four views (think/act × human/rational), a brief history from Turing to transformers, the state of the art, and where this course sits.
chapter 2Agents, sensors, actuators, rationality, the PEAS framework, all six environment axes with real examples, and the five agent types from reflex to learning.
chapter 3Problem formulation, state spaces, the search tree, frontier strategies. BFS, DFS, UCS, IDS — how each works, why each fails, and the complexity you must know cold.
chapter 4Heuristics, Greedy BFS, A* and the admissibility/consistency proofs, relaxed problems, pattern databases. Local search, hill climbing, simulated annealing, and genetic algorithms.
chapter 5Games as search, the game tree, minimax and why "maximise the minimum" works, static evaluation, alpha-beta pruning step by step, and Monte Carlo Tree Search with UCB.
What computational intelligence is, how it differs from classical AI, the five CI paradigms — neural networks, evolutionary computation, swarm intelligence, fuzzy systems, immune systems — and why nature is a good teacher.
chapter 5Darwin in code — how evolution becomes an optimiser. Genetic algorithms in depth: representation, fitness, roulette-wheel selection, crossover, mutation. Building blocks hypothesis and schema theorem.
chapter 6Birds, not ants — a swarm of particles flying through the search space, each pulled by its own best memory and the swarm's best. Velocity and position updates, inertia and acceleration coefficients, gbest vs lbest topologies, and a worked iteration.
chapter 7Stigmergy, how real ants find short paths, and how ACO turns that into an optimiser. Full TSP walkthrough with pheromone tables, evaporation and update arithmetic, parameter tuning.