Analysis of Game Tree Search Algorithms Using Minimax Algorithm and Alpha-Beta Pruning

Abstract

An important topic of research in computer systems is the optimization of finding the optimum course of action based on different variables, such as the environment's state, the system's goal, etc. The building of the entire state search space, also known as the minimax algorithm, can result from any search algorithm's attempt to find the best feasible solution from among all known possibilities. The recursive backtracking algorithm known as Minimax is used to select the next action in a game of strategy for two players. The algorithm works well because it anticipates that your adversary will play well as well. However, as the tree's depth increases, we observe that minimax frequently investigates repetitive and unlikely situations. We'll also take a look at the minimax extension known as alpha-beta pruning, which prohibits us from considering states that won't be chosen. We will also examine a number of established techniques for resolving two-player games, such as adversarial search and other machine learning-based techniques.