How To Master Alpha-Beta Pruning: Understanding The Basics With 8 Simple Steps Gameplay AI technique

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Alpha-beta pruning is a powerful technique used to optimize decision-making in game-playing AI. Learn how it reduces the number of branches searched and improves performance in turn-based, two-player games like chess and tic-tac-toe
How To Master Alpha-Beta Pruning: Understanding The Basics With 8 Simple Steps Gameplay AI technique, How is min max calculated
In this video, we will dive into the world of Alpha-beta pruning, a powerful technique used to optimize decision-making in game-playing AI. You will learn how it works, how it reduces the number of branches searched and improves performance in turn-based, two-player games like chess and tic-tac-toe. By the end of this video, you will have a clear understanding of this essential technique for game-playing AI
function minimax(node, depth, alpha, beta, maximizingPlayer) is
if depth ==0 or node is a terminal node then
return static evaluation of node

if MaximizingPlayer then // for Maximizer Player
maxEva= -infinity
for each child of node do
eva= minimax(child, depth-1, alpha, beta, False)
maxEva= max(maxEva, eva)
alpha= max(alpha, maxEva)
if beta is less=alpha
break
return maxEva

else // for Minimizer player
minEva= +infinity
for each child of node do
eva= minimax(child, depth-1, alpha, beta, true)
minEva= min(minEva, eva)
beta= min(beta, eva)
if beta isless =alpha
break
return minEva

// is less than used instead of symbol because you tube is not allowing that sybmol

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