This three-lesson plan provides students with a comprehensive understanding of Minimax Algorithms and their optimization through Alpha-Beta Pruning. Through theoretical exploration and practical implementation, students will enhance their problem-solving skills in adversarial settings and gain valuable insights into computational efficiency.
This lesson plan outlines the introduction to the Minimax Algorithm, a key concept within game theory used in decision-making processes for adversarial games. By the end of the lesson, students will understand the basic principles of the algorithm, how it applies to two-player games, and its limitations.
Lesson 2: Understanding Alpha-Beta Pruning
Overview
In this lesson, we will dive into the concept of alpha-beta pruning as an optimization technique for the minimax algorithm, a cornerstone of decision making in artificial intelligence. We will evaluate how alpha-beta pruning decreases the number of nodes the algorithm needs to examine, thus enhancing computational efficiency. Through practical examples and engaging discussions, students will gain a comprehensive understanding of this technique and its significance in AI.
Lesson 3: Application and Implementation of Minimax with Alpha-Beta Pruning
Overview
In this lesson, students will gain hands-on experience with the optimized minimax algorithm with alpha-beta pruning by implementing it in a coding environment. They will then apply the algorithm to sample game situations, aiming to understand its efficiency in decision-making processes within gameplay. The class will conclude with a discussion on performance metrics and potential improvements.
