What is minimax algorithm and alpha-beta pruning?

Alpha-beta pruning is a procedure to reduce the amount of computation and searching during minimax. Minimax is a two-pass search, one pass is used to assign heuristic values to the nodes at the ply depth and the second is used to propagate the values up the tree. Alpha-beta search proceeds in a depth-first fashion.

How do you implement alpha-beta pruning in minimax?

How does alpha-beta pruning work?

  1. Initialize alpha = -infinity and beta = infinity as the worst possible cases.
  2. Start with assigning the initial values of alpha and beta to root and since alpha is less than beta we don’t prune it.
  3. Carry these values of alpha and beta to the child node on the left.

Is alpha-beta pruning better than minimax algorithm?

The Alpha-beta pruning to a standard minimax algorithm returns the same move as the standard algorithm does, but it removes all the nodes which are not really affecting the final decision but making algorithm slow. Hence by pruning these nodes, it makes the algorithm fast.

What is alpha-beta pruning with example?

Alpha Beta Pruning is a method that optimizes the Minimax algorithm. The number of states to be visited by the minimax algorithm are exponential, which shoots up the time complexity. Some of the branches of the decision tree are useless, and the same result can be achieved if they were never visited.

What is minimax algorithm in AI?

The min max algorithm in AI, popularly known as the minimax, is a backtracking algorithm used in decision making, game theory and artificial intelligence (AI). It is used to find the optimal move for a player, assuming that the opponent is also playing optimally.

How does minimax algorithm work?

Mini-max algorithm is a recursive or backtracking algorithm which is used in decision-making and game theory. It provides an optimal move for the player assuming that opponent is also playing optimally. Mini-Max algorithm uses recursion to search through the game-tree.

How does Python implement minimax algorithm?

Programming Minimax Lets implement a minimax search in python! We first need a data structure to hold our values. We create a Node class, it can hold a single value and links to a left and right Node . Then we’ll create a Choice class that represents the players choice.

What is minimax algorithm in Python?

Minimax is a type of adversarial search algorithm for generating and exploring game trees. It is mostly used to solve zero-sum games where one side’s gain is equivalent to other side’s loss, so adding all gains and subtracting all losses end up being zero.

Why is alpha-beta pruning an improvement over naive minimax search?

Improvements over native minimax The benefit of alpha–beta pruning lies in the fact that branches of the search tree can be eliminated. This way, the search time can be limited to the ‘more promising’ subtree, and a deeper search can be performed in the same time.

What is minimax algorithm explain in detail?

Minimax is a kind of backtracking algorithm that is used in decision making and game theory to find the optimal move for a player, assuming that your opponent also plays optimally. It is widely used in two player turn-based games such as Tic-Tac-Toe, Backgammon, Mancala, Chess, etc.

What is pruning in AI?

The word pruning means trimming or cutting away the excess; in the context of machine learning and artificial intelligence, it involves removing the redundant or the least important parts of a model or search space.

How do you make a game tree in Python?

Program to fill Min-max game tree in Python

  1. Define a function helper() .
  2. if root is empty, then.
  3. helper(left of root, h, currentHeight + 1)
  4. helper(right of root, h, currentHeight + 1)
  5. if currentHeight < h, then.
  6. Define a function height() .
  7. if root is null, then.