How Game AI Makes Decisions — From Minimax to Alpha-Beta Pruning

Short summary

Game AI differs from pathfinding because you must account for an opponent's responses, not just your own moves. Minimax structures opponent-aware decision-making as recursive search assuming both players play optimally, choosing moves with the best worst-case outcomes. Alpha-Beta Pruning optimizes this by skipping branches that cannot affect the final decision, enabling deeper searches without exponential cost.

• •Minimax enables opponent-aware game search by recursively evaluating future states and assuming optimal play
• •The algorithm chooses moves with the best worst-case outcomes, not just immediately high scores
• •Alpha-Beta Pruning optimizes Minimax by skipping unnecessary branches, maintaining the same result with lower computational cost

Generated with AI, which can make mistakes.