Game AI with minimax

We want games to feel smart, but not too smart. That’s where game trees and the minimax algorithm come in. They make the AI look like she’s planning ahead, even if she’s just running the numbers fast.

Game trees

Think of a game tree as a map of “if we do this, then they’ll do that.” Each branch is a possible move. Each level is a turn. The tree keeps spreading until we run out of space or patience.

The neat part: we can evaluate the end points. Win = good score, loss = bad score. Draw = somewhere in between. Simple math, but it gives us a way to compare paths.

The minimax idea

Minimax is a pessimist. She assumes the opponent will always make the best move against us. So we pick our move by looking ahead, predicting her counter, and then choosing the least bad outcome.

It’s like buying an umbrella not because we think it will rain, but because if it does, we’ll regret it less. That’s minimax.

Depth and pruning

In practice, trees get huge. Even tic-tac-toe fills quickly, and chess explodes. We can’t explore every branch, so we set a depth limit. Look three moves ahead, not thirty.

We also use alpha-beta pruning. That’s a fancy way of saying “don’t bother checking dead ends.” If one branch is clearly worse, skip it. Saves time, same result.

Why it works

Players don’t expect perfection. They expect mistakes, but not stupid ones. Minimax, trimmed and shallow, gives us that balance. She looks clever without crushing us every time.

A coder’s musing

We like that something so basic feels like strategy. It’s just search plus math, yet on-screen she seems thoughtful. Maybe that’s the trick: not building real thought, just enough of it to keep us guessing.