Artificial Intelligence has the most demand in today’s time. Artificial Intelligence uses high computing programs to get development in the market. It is a mixture of Neural Networks, Data, and a high level of coding. That helps in the implementation of complex algorithms.

Computers are smart enough to read the human minds. It was us who invented computers and gave them the most high-tech program, language, and codes. These programs built to make human life simple and easy, but it seems opposite.

Minimax is an algorithm that is used in Artificial intelligence. Minimax uses a backtracking algorithm or a recursive algorithm that determines game theory and decision making. It uses the flowchart of a game tree. From which it will decide automatically to use the min function or the max function responsibly. 

It is the algorithms that make the functioning of Artificial Intelligence possible. They work in a sequence of unambiguous commands. That helps to perform calculations, solve the problem, and much more specified actions. Where there is a game program, the Minimax algorithm comes first in all the programmers’ minds.

The Minimax algorithm is developed only for two-player games. Some of the two-player games are as follows. The most popular are poker, chess, tic tac toe, checkers, and much more. These all games are based on two players. The computer understands and implements the function of the game tree formula. To make its move to the opponents.

The minimax algorithm finds the player’s optimal move, domineering that the rival player is playing optimally too. The algorithm works at a time and only makes a move until the opponent makes one.

The Advantages of using a Game Tree in Minimax Algorithm

A Game Tree helps us to know the actual working of the Minimax algorithm in Artificial Intelligence. The main aim of creating a game tree is to find the best move for a player to win or lose a game. The maximizer will select the maximized value in the game, and the minimizer will select the minimized value. In the end, the goal is, one player will win, and the other player has to lose.

In this algorithm, the function minimax will decide who wins or lose. Both the game players will try to win the game, but the only one can win. One will get to achieve minimum benefits or least score in the game. Simultaneously, the other player will get to achieve the maximum benefits or highest score in the game.

In every two-player game. There is a scoreboard that determines winning and losing. So, in a counter move, a minimizer has to choose a minimized value. And the maximizer has to choose a maximized value. According to the rule, the negative value can be only attained by the minimizer only if it has the upper hand. The positive value can be only attained by the maximizer only if it has the upper hand.

Flowchart of a Game Tree

The game tree above you sees it is evaluating the moves that we play in a two-player game. It starts with the most important node, which we call the Root node. This node represents Level 0. Later on, the roots of level 0 move into the Parent node. This node represents Level 1. As we can see from the data above, the node gets divided further into the Child node. This node represents Level 2, and it goes on and on till infinite.

Level 0 is considered to be the current state of the game. Whenever a player 2 makes a move in the game. It assumes that the game is in the root node waiting for player 1 to make a move. In the parent node, i.e., Level 1, it holds all the attainable moves of player 1. In the Child node, i.e., Level 2, it holds all the attainable moves of player two build on each attainable move of player 1.

It is how a Minimax algorithm works in an Artificial intelligence two-player game. All the algorithms used in the game will follow the node accordingly and as per player 2. The computer will make the moves for player 1, resulting in winning and losing the game.

Advantages in the Game Tree

  • It is very easy for Artificial Intelligence to make decisions and game theory.
  • Due to the Minimax algorithm’s help, it is possible to invent smart and efficient machines, which have the capabilities to learn faster.
  • The Minimax algorithm performs a deep evaluation in the field of search space.
  • It makes the working of two-player games much smooth, and zero chances of any glitch due to the Minimax algorithm’s accuracy.

Properties of Minimax algorithm in Artificial Intelligence

  • It optimally leads the game. Meaning there is no cheating in the game.
  • The time complexity of the Minimax algorithm becomes 0(BM). Bm stands for Branching factor and Maximum depth in the game tree, and all this is possible with the help of (DFS) Depth First Search.
  • The Minimax algorithm finds a complete solution with the help of a game tree, which will ensure the evaluation of the moves made by both players.

Final Thoughts

This article will surely help you to understand the working of the Minimax algorithm in the world of Artificial Intelligence. The working and the advantages are mentioned in the article above. It also discusses the properties that are derived from using Game Tree. Artificial Intelligence is one of the major parts of our life. It is the technology that made it possible, which helps in solving some complex problems. In the world of humankind by computers.