Minimax Here

This is an optimization technique that "cuts off" branches in the game tree that don't need to be searched. If the algorithm finds a move that is significantly worse than a move it already discovered, it stops calculating that path entirely. This allows the AI to search much deeper in the same amount of time. 2. Heuristic Evaluation Functions

To understand Minimax, you have to look at the game as a . The current state of the game is the "root," and every possible move creates a "branch" leading to a new state. 1. The Maximizer (You) minimax

There are two participants (traditionally called "Maximizer" and "Minimizer"). This is an optimization technique that "cuts off"