Understanding Alpha-Beta Algorithm and Optimal Game Strategies

This week's session focuses on planning and strategies in artificial intelligence search methods.

The first question discusses the identification of alpha and beta cutoff points in the alpha-beta algorithm.

The second question involves selecting valid game strategies for a root node in a game tree.

The third question requires filling up a game tree to determine the outcome from the Max's perspective.

💡 The video discusses the concept of game tree and the Min-Max strategy.

🌳 A game tree is a graphical representation of all possible moves and outcomes in a game.

⚖️ The Min-Max strategy is a decision-making algorithm used in games, where players try to minimize their maximum possible loss.

🔑 The best strategy consists of nodes 3, 4, 5, and 6, with values 68, 68, 72, and 72 respectively.

📝 When choosing a child of a Max node, we choose one child, while for a Min node, we choose all its children.

🔍 The Alpha Beta algorithm involves initializing Alpha as -∞ and Beta as +∞, passing them to each child, and updating them based on the values obtained.

🔑 The Alpha-Beta method is used to optimize the search process in a game-tree.

💡 Nodes in the game-tree have their own Alpha and Beta values that change as the algorithm progresses.

📉 Alpha cutoff occurs when the Alpha value is greater than or equal to the Beta value, resulting in a subtree being pruned.

🎯 The video explains the Alpha-Beta algorithm and how it reduces the amount of effort required in strategies by pruning unnecessary branches.

🔀 The algorithm assigns values to nodes in a game tree and compares the alpha and beta values to determine whether to prune branches.

📊 By using the Alpha-Beta algorithm, the video shows that it was able to solve the game tree with a value of 68, resulting in 1 beta cutoff and 6 alpha cutoffs.

⭐ There are four strategies for each subtree, resulting in a total of 12 strategies.

🌳 The SSS star algorithm starts with initial clusters formed from the Horizon nodes and uses a priority queue to determine the node to solve next.

🔍 The SSS star algorithm selects all possible children for a Max node and one child for a Min node, starting with the leftmost child.

📌 The SSS* algorithm is used to solve optimization problems.

🔢 The algorithm assigns values to nodes based on their priority and solves them accordingly.

🌳 The algorithm prunes unnecessary sub-trees to optimize the solution.