MINMAX ALGORITHM in machine learning.pptx

this ppt is used to learn about minmax algorithm

1. MINMAX ALGORITHM BY NAME : SURYAKUMARAN M ROLL.NO : 24CSER025 UNIT.NO: 2 SUBJECT: FOUNDATION OF ARTIFICIAL INTELLIGENCE

2. WHAT IS MINMAX ? The MinMax algorithm, also known as 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. The algorithm is mostly employed for game play, such as chess, checkers, tic-tac-toe, go, and other two-player games

3. WORKING OF MIN-MAX PROCESS IN AI The Min-Max algorithm is a decision-making process used in artificial intelligence for two-player games. It involves two players: the maximizer and the minimizer, each aiming to optimize their own outcomes . Maximizing Player (Max ): Aims to maximize their score or utility value. Chooses the move that leads to the highest possible utility value, assuming the opponent will play optimally .

4. Cond… Minimizing Player (Min ): Aims to minimize the maximizes score or utility value. Selects the move that results in the lowest possible utility value for the maximize, assuming the opponent will play optimally.

5. EXAMPLE OF MIN-MAX IN ACTION Consider a simplified version of a game where each player can choose between two moves at each turn. Here’s a basic game tree : Max / \ Min Min / \ / \ + 1 -10 + 1

6. Cond.. At the leaf nodes, the utility values are +1, -1, 0, and +1. The minimizing player will choose the minimum values from the child nodes: -1 (left subtree ) and 0 (right subtree ). The maximizing player will then choose the maximum value between -1 and 0, which is 0.

7. ALPHA-BETA PRUNING OPTIMIZATION IN MINI-MAX ALGORITHM Alpha-beta pruning enhances the Min-Max algorithm by eliminating branches that do not affect the final decision . Alpha (α):  The best value that the maximizing player can guarantee so far. Beta (β):  The best value that the minimizing player can guarantee so far. During the search: If \alpha \ geq \beta, prune the remaining branches.

8. STRENGTHS OF THE MIN-MAX ALGORITHM Optimal Decision Making : The Min-Max algorithm ensures optimal decision making by considering all possible moves and their outcomes. It provides a strategic advantage by predicting the opponent’s best responses and choosing moves that maximize the player’s benefit .

9. Cond.. Simplicity and Clarity : The Min-Max algorithm is conceptually simple and easy to understand. Its straightforward approach of evaluating and propagating utility values through a game tree makes it an accessible and widely taught algorithm in AI.

10. WEAKNESSES OF THE MIN-MAX ALGORITHM Computational Complexity:  The primary drawback of the Min-Max algorithm is its computational complexity. As the depth and branching factor of the game tree increase, the number of nodes to be evaluated grows exponentially. This makes it computationally expensive and impractical for games with deep and complex trees, like Go.

11. Cond… Depth Limitations: To manage computational demands, the Min-Max algorithm often limits the depth of the game tree. However, this can lead to suboptimal decisions if critical moves lie beyond the chosen depth. Balancing depth and computational feasibility is a significant challenge.

12. Cond… Handling of Uncertain Environments :   The Min-Max algorithm assumes deterministic outcomes for each move, which may not be realistic in uncertain or probabilistic environments. Real-world scenarios often involve uncertainty and incomplete information, requiring modifications to the basic Min-Max approach.

13. CONCLUSION In summary, the  minimax algorithm helps the AI make optimal decisions by considering the best and worst possible outcomes for each move, assuming both players play perfectly.

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