BE - Artificial Intelligence

Min-Max search is a decision-making algorithm used in two-player games where one player tries to maximize their score while the opponent tries to minimize it. It's like looking ahead at all possible moves and counter-moves to choose the best strategy, assuming your opponent will play perfectly against you. 
How It Works 

The algorithm builds a game tree where each node represents a game state. Starting from the current position, it explores all possible moves several steps ahead, alternating between "Max" levels (your best moves) and "Min" levels (opponent's best counter-moves). Each position is scored using an evaluation function, and the algorithm works backward to find the optimal move. 

Key Process 

    Build tree: Create all possible game positions for several moves ahead
    Score positions: Use evaluation function to rate each position
    Choose moves: Max picks highest scores, Min picks lowest scores
    Work backward: Propagate best scores up the tree
    Select move: Pick the initial move leading to the best outcome
     

Practical Implementation 

Since exploring all possibilities is impossible, Min-Max uses depth limits and evaluates positions at that depth. Alpha-Beta pruning optimizes the search by eliminating branches that won't affect the final decision, making searches faster and deeper. 
Applications 

Used in chess programs, video game AI, strategic planning, and any competitive scenario requiring lookahead decision-making. Famous for powering chess computers that defeated world champions. 
Advantages and Limitations 

Pros: Guarantees optimal play, systematic approach, adaptable to different games
Cons: Computationally expensive, depends on evaluation quality, limited to perfect information games