Satyam Mahapatra presents on the Minimax algorithm in artificial intelligence. The Minimax algorithm is a decision-making algorithm for two-player games that finds the optimal next move. It works by having one player act as the maximizer to achieve the highest score and another as the minimizer to achieve the lowest score, based on the zero-sum concept where one player's win equals the other's loss. The algorithm constructs a game tree, evaluates leaf node scores, and backs up scores from leaves to the root while considering player type to select the child node with the best score for the current player. It is commonly used in games like tic-tac-toe, chess, and checkers that involve turns between players

1. Name- Satyam Mahapatra
Department:- B.Tech CSE-1 Univ Roll.No :- 12000220031
Year :- 3rd Semester :- 5th Clg Roll.No :- 2003140
Subject:-Artificial Intelligence Subject Code :- PEC-IT501B
TOPIC :- MINIMAX ALGORITHM IN AI
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2. Flow of Presentation
● Introduction
● Principle
● Description
● Example
● Application
● Advantage/Disadvantage
● Conclusion
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3. Introduction to Minimax
● Minimax is a decision- making algorithm typically in a 2 player game.
● Minimax is a kind of backtracking algorithm.
● The goal of the algorithm is to find the optimal next move.
● In the algorithm, one player is called maximizer and another is called minimizer.
● The maximizer works to get the highest score, while the minimizer works to get the
lowest score.
● It is based on the zero-sum game concept, whenever one player wins other loses. So
the total score is always zero.

4. Principle
In two player games algorithm works as:
● The two players are moving towards opposite goals.
● The opponent will try to minimize whatever the algorithm is trying to minimize, hence
“minimax”.
● The goal is to find the best move for the player.

5. Description
● Construct the complete game tree.
● Evaluate scores for leaves using the evaluation function.
● Backup scores from leaves to root considering the player type:
1) For max player select the child with maximum score.
2) For min player select the child with minimum score.
● At the root node, choose the node with max value and perform the corresponding move.
● Start evaluating max with worst value of -infinity.
● Start evaluating max with worst value of +infinity.

6. Example
In this example, R is a player 1 maximizer and B is a player 2 minimizer.Here +infinity is a win
for R and -infinity is a win for B.
Basis: assume opponent best move.
Task: to find the best move for max.
Assume: max moves first and then min moves thus alternatively.
Type: win, lose or draw
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7. Tic-Tac-Toe:
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8. Application :
Minimax is the heart of almost every computer board game.It applies to the games where
● Players take turns
● Underlying assumption
● Have perfect information
But it can work for games with or without perfect information or chance e.g:
● Poker
● Monopoles
● Dice
● Tic-Tac-Toe
● Chess
● Checkers

9. Advantages:-
● Returns an optimal action, assuming perfect opponent play.
● Minimax is the simplest possible game search algorithm.
● It provides complete possible search if tree is finite.
Disadvantages:-
● It is completely in feasible in practice.
● When search tree is too large we need to limit the search depth.
● We have to apply an evaluation function to the cut-off states.

10. Conclusion :-
Minimax algorithm is one of the most popular algorithms for computer
board games. It is widely applied in turn based games. It can be a good
choice when players have complete information about the game.
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11. References :-
● https://en.wikibooks.org/wiki/Artificial_Intelligence/Search/Adversarial_search/Minimax_
Search
● https://www.baeldung.com/java-minimax-algorithm
● https://www.hackerearth.com/blog/developers/minimax-algorithm-alpha-beta-pruning/
● https://www.freecodecamp.org/news/playing-strategy-games-with-minimax-4ecb83b39
b4b/
Thank You