Minimax Algorithm in AI Theory

Summary:
The minimax algorithm is a fundamental decision-making strategy widely used in artificial intelligence, particularly in games and adversarial systems. This article explains how it works, where it’s applied, and how its legacy continues in models like MiniMax M1.

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What Is the Minimax Algorithm?

At its core, the minimax algorithm is a recursive decision rule that minimizes the possible loss for a worst-case scenario. It is most commonly found in:

• Chess AI and other board games
• Turn-based decision-making systems
• Game theory and economic models

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Basic Principle

Minimax alternates between maximizing and minimizing strategies:

• One player tries to maximize gain
• The opponent tries to minimize it

This leads to a tree of outcomes that are evaluated recursively until the best possible decision is selected.

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Example in Game Theory

In a game like Tic-Tac-Toe, the AI:

• Explores all possible moves and counter-moves
• Uses evaluation functions to score end states
• Chooses the path that ensures the best worst-case outcome

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Minimax in Modern AI

While no longer the core engine of large language models, the minimax algorithm influences:

• 🎮 Reinforcement learning strategies
• 🤖 Adversarial training setups
• 🧠 Decision planning in agents

Even in the naming of MiniMax M1, there's a nod to balanced optimization and outcome awareness.

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Pseudocode Reference

def minimax(node, depth, is_max):
    if node is terminal:
        return evaluate(node)
    if is_max:
        return max(minimax(child, depth-1, False) for child in node.children)
    else:
        return min(minimax(child, depth-1, True) for child in node.children)