Download
1 / 8
80 likes | 218 Vues
This document delves into the MiniMax search algorithm, illustrating its mechanics through a detailed example. The process begins with the exploration of nodes and labels, expanding the search space to a depth of three-ply. We examine how values are propagated backward up the decision tree, first selecting maximum values at Max levels and then minimum values at Min levels. By demonstrating this iterative value selection process, we elucidate how the MiniMax algorithm culminates in making optimal moves. The final choice obtained by Max highlights the effectiveness of this foundational strategy in game theory.
E N D
Max Min Max Min Starting node and labels MiniMaxSearch
Max Min Max Min Continue expand the search space
Max Min Max Min Continue expand the search space
Max Min Max Min 2 3 5 9 0 7 4 Expand the search space down 3-ply
Max Min 9 0 3 7 7 Max Min 2 3 5 9 0 7 4 Propagate the leaf values backward Pick Max values at Max Level
Max 3 0 Min 9 0 3 7 7 Max Min 2 3 5 9 0 7 4 Continue propagate the values backward Pick Min values at Min Level
3 Max 3 0 Min 9 0 3 7 7 Max Min 2 3 5 9 0 7 4 Continue propagate the values backward Pick Max values at Max Level
3 Max 3 0 Min 9 0 3 7 7 Max Min 2 3 5 9 0 7 4 The move picked by Max
