Delve into the world of game strategies and decision-making processes in zero-sum games like Tic-Tac-Toe, Checkers, and Chess. Discover how computers can analyze and optimize moves using algorithms like Minimax, creating challenging gameplay experiences. Learn about decision trees and the concept of

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Explore the significance of studying games, origins of game AI algorithms like Minimax, Alpha-beta search, and more. Learn about types of game environments, zero-sum games, strategies in two-player games, and game trees. Delve into the complexity of game theory and the evolving landscape of artifici

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Creating an AI program using the minimax algorithm to challenge competent human players in the game of Hex. Tasks include board state representation, user interface design, win condition testing, and utilizing Dijkstra's algorithm for static evaluation. The AI builds decision trees to determine opti

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Adversarial search in AI involves making optimal decisions in games through concepts like minimax and pruning. It explores the strategic challenges of game-playing, from deterministic turn-taking to the complexities of multi-agent environments. The history of computer chess and the emergence of huma

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Adversarial reasoning in games, particularly in the context of artificial intelligence, involves making optimal decisions in competitive environments. This module covers concepts such as minimax pruning, game theory, and the history of computer chess. It also explores the challenges in developing AI

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Competitive games create conflict between agents, leading to adversarial search problems. The Minimax algorithm, used to optimize player decisions, plays a key role in analyzing strategies. Studying games offers insights into multiagent environments, economic models, and intellectual engagement. The

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