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Explore game-playing algorithms in artificial intelligence through this comprehensive lecture that covers minimax and alpha-beta pruning techniques. Begin with fundamental game properties and the evolution of game-playing computers, then delve into look-ahead evaluation strategies and complete game tree searching. Master the minimax algorithm by understanding two-player search trees, maxvalue and minvalue functions, and examine the more efficient negamax variant. Learn alpha-beta pruning optimization techniques through detailed tree examples that demonstrate significant computational savings, and discover how to shorten the algorithm while recording optimal actions. Advance to iterative deepening alpha-beta methods and review practical pseudocode for assignment implementation, concluding with exam preparation materials that reinforce key algorithmic concepts for competitive game AI development.
