How machines calculate the perfect move in adversarial games
In short: Explore the foundational logic behind computer chess. This lecture from MIT's Artificial Intelligence course breaks down how algorithms navigate complex decision trees to outmaneuver opponents.
Instructor Patrick Winston explores strategies for adversarial games, focusing on the minimax algorithm as a primary framework for determining optimal moves. To handle the massive complexity of games like chess, the lecture introduces alpha-beta pruning, a technique designed to increase computational efficiency by eliminating branches of the search tree that cannot possibly influence the final decision.
The session also covers progressive deepening, a method that ensures a system always provides a viable answer by managing search depth. These concepts form the backbone of how AI evaluates competing interests and navigates high-stakes, multi-turn environments.
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Sources
- Summary by Intellect · source: 6. Search: Games, Minimax, and Alpha-Beta
- Also: Lecture 6: Search: Games, Minimax, and Alpha-Beta | Artificial Intelligence | Electrical Engineering and Computer Science | MIT OpenCourseWare
- Also: Lecture 6: Search: Games, Minimax, and Alpha-Beta | MIT Learn
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