The opening arc of modern AI: what intelligence means, how rational agents are designed, and how search turns goals into actions in single-agent, complex, and adversarial worlds. Every finished chapter is a detailed, story-led master's companion with a first-principles map, central derivation, worked case, failure modes, fair evaluation plan, study lab, oral-exam questions, and complete summary.
Definitions of AI, rational action, the intellectual foundations and history of the field, present capabilities, and the benefits and risks of building intelligent systems.
chapter 2Agents and environments, rationality, PEAS task specifications, environment properties, reflex and model-based architectures, goals, utilities, learning agents, and safe task design.
Problem formulation, state spaces, tree and graph search, breadth-first and depth-first search, uniform-cost search, iterative deepening, best-first search, A*, heuristics, and search evaluation.
chapter 4Local search, hill climbing, simulated annealing, local beam search, evolutionary search, continuous optimization, nondeterministic actions, partial observability, online search, and unknown environments.
chapter 5Game trees, minimax, alpha-beta pruning, move ordering, cutoff search, evaluation functions, Monte Carlo tree search, stochastic games, partial observability, and multiplayer settings.