لخّصلي

Many researchers have addressed the use of search algorithms in artificial intelligence, and studies have focused on improving the efficiency of artificial intelligence in analyzing in game analytics.This will contribute to improving the design of more intelligent and interactive artificial intelligence systems within games.Wang et al. (2021) noted that incorporating algorithms such as A* and Minimax contributed to improving AI's ability to analyze complex environments and predict player behavior.Hence, the idea of this research was to fill this gap by analyzing and evaluating how search algorithms are applied to several game models, and comparing their effectiveness in terms of speed, accuracy, and adaptability to changing environments."The name "minimax" comes from minimizing the loss involved when the opponent selects the strategy that gives maximum loss and is useful in analyzing the first player's decisions both when the players move sequentially and when the players move simultaneously".Recent studies show growing interest in using search algorithms to improve AI performance in digital games, particularly games that rely on multiple decision-making, such as chess and role-playing games.

Many researchers have addressed the use of search algorithms
in artificial intelligence, and studies have focused on
improving the efficiency of artificial intelligence in analyzing
in game analytics. "The name "minimax" comes from minimizing
the loss involved when the opponent selects the strategy that gives
maximum loss and is useful in analyzing the first player's decisions
both when the players move sequentially and when the players move
simultaneously".
A* search algorithm is directed algorithm which means it does not
blindly search for a path but instead it calculates the best direction to
move and then it can backtrack the path. It will not only find a path
between source and destination, but it will find the shortest path
quickly.
Recent studies show growing interest in using search algorithms to
improve AI performance in digital games, particularly games that rely
on multiple decision-making, such as chess and role-playing games.
Wang et al. (2021) noted that incorporating algorithms such as A*
and Minimax contributed to improving AI's ability to analyze
complex environments and predict player behavior. Another study by
Kim (2020) demonstrated that the effectiveness of algorithms depends
largely on the design of the in-game environment and the complexity
of the scenarios the AI deals with.
Despite this progress, most studies have focused on specific games
without expanding to different environments or comparing the
performance of algorithms across multiple game types. Hence, the
idea of this research was to fill this gap by analyzing and evaluating how search algorithms are applied to several game models, and
comparing their effectiveness in terms of speed, accuracy, and
adaptability to changing environments. This will contribute to
improving the design of more intelligent and interactive artificial
intelligence systems within games.