Abstract

Checkers is a strategy game for two players on an 8×8 square board. This document outlines the creation of a checkers game, employing alpha-beta pruning as a search algorithm and transposition tables to enable the game to learn from past play sessions. The utilization of these techniques enhances the game's strategic decision-making process, contributing to an improved overall gaming experience. The execution time of the machine's moves in games with and without using transposition tables showed that it is possible to make some games faster by using the data stored in the tables. However, under some conditions this can be more time-consuming than the search performed by the alpha-beta algorithm. A user survey resulted in positive outcomes, indicating a favorable user experience when using the implemented game with the transposition tables. Comparative analysis against other algorithms demonstrated the successful implementation of the approach. This research contributes to the field of game AI optimization by integrating alpha-beta pruning and transposition tables into a web-based checkers game. The implementation offers a balance between computational efficiency and user engagement in the web-based checkers game.