From the course: AI Algorithms for Gaming
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The minimax algorithm - Python Tutorial
From the course: AI Algorithms for Gaming
The minimax algorithm
- Now let's see the Pseudocode for Minimax, as seen in the celebrated book, Artificial Intelligence A Modern Approach by Peter Norvig and Stuart Russell. First, recall that we use Minimax to produce a move, or more formally an action to take next at a given state of the game. But minimax is an optimization algorithm that produces a number, a score. So the decision algorithm for Minimax is just a wrapper for the function that implements the top max node. So in line one, we have the declaration of this minimax decision function, which takes a state as argument and returns an action. Line two, call to the function that implements the max node decision. This function is called Max Value, and as you can see it takes the same state of the game as argument. The result of this function is this score chosen by the max node and it's assigned to a value variable V. In line three, we return the action among the successors or…
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