Checkers-AI | 1 | American Checkers Game Using Game Theory and Artific

The method of decision-making is a process involving decision-makers, actors, environmental variables, priorities, policies, and parameters. With efficient and accurate game theory and artificial intelligence (AI) algorithms, it is now possible to solve games involving a decision-making method with a huge set of possibilities. Checkers is a zero-sum game that is a statistical situation in which the benefit or loss in value of each player is exactly accounted for by the losses or gains in utility of the other players with a game-tree complexity of about 1040. In this paper, a heuristic function is used to rank alternative branching points in search algorithms based on the current evaluated position to decide which branch to explore is proposed to enable the algorithm search more rapidly. A Binary Search tree is applied to search for the best step in order to implement the one-player checkers game. Since the search area is huge in a practical game like checkers, this project is implemented using the Minimax approach and Alpha-Beta pruning. This paper also provides an in-depth and thorough study of methodologies and algorithms in game theory, such as their comparative analyses, including heuristic function optimizations and best-case analysis for Alpha-Beta pruning. In addition, a playable game with a graphical user interface (GUI) for visual representation and the operation of applied algorithms is also implemented using HyperText Markup Language (HTML) and JavaScript.