Understanding Minimax and Alpha-Beta Pruning in Connect 4

What is the primary goal of the Minimax algorithm in two-player games?

• To determine the best move by evaluating all possible outcomes

• To end the game as quickly as possible

• To minimize the opponent's score

• To maximize the number of moves

Why is the Minimax algorithm inefficient for Connect 4?

• It doesn't consider the opponent's moves

• It only works for games with fewer than 5 moves

• It requires too much memory

• The number of possible game states grows exponentially

How can the efficiency of the Minimax algorithm be improved in Connect 4?

• By ignoring the opponent's moves

• By using a random move generator

• By exploring all possible game states

• By assigning weights to board positions and limiting exploration depth

What is the purpose of Alpha-Beta pruning in the Minimax algorithm?

• To ensure the algorithm always wins

• To reduce the number of nodes evaluated

• To increase the number of nodes evaluated

• To make the game more challenging

In Alpha-Beta pruning, what does the alpha value represent?

• The best score the minimizing player can achieve

• The worst score the maximizing player can achieve

• The best score the maximizing player can achieve

• The average score of all possible moves

What happens when the alpha value is greater than or equal to the beta value in Alpha-Beta pruning?

• The algorithm resets the alpha and beta values

• The algorithm continues to explore all branches

• The algorithm switches to a different game

• The algorithm prunes the branch to save computation time

How does Alpha-Beta pruning affect the performance of the Minimax algorithm?

• It makes the algorithm less accurate

• It has no effect on performance

• It makes the algorithm slower

• It speeds up the algorithm by pruning unnecessary branches

In the best-case scenario, how much faster can Alpha-Beta pruning make the Minimax algorithm?

• It doesn't affect the speed

• Four times faster

• Three times faster

• Two times faster

What is a potential downside of Alpha-Beta pruning?

• It makes the algorithm less accurate

• It always prunes too many branches

• It may not prune any branches if scores are sorted from least to best

• It requires more memory

What is the main advantage of using Alpha-Beta pruning with the Minimax algorithm?

• It simplifies the game rules

• It guarantees a win in every game

• It eliminates the need for an evaluation function

• It reduces computation time while maintaining accuracy