| Resumen |
This article proposes an Ant Colony Optimization (ACO) algorithm, an optimization method to find paths in graphs, adapted to solve strategic games. The games of study are Tic-Tac-Toe (also known as noughts and crosses, three in a row, or Xs and Os), and Chess. The algorithms' performance is contrasted by contending ACO against the Minimax algorithm, in different setups of Tic-Tac-Toe and Chess. The performance is explained in terms of average time response, correctness of the move choice, and memory used when executing the function. Results reveal a slightly better average performance by the ACO algorithm compared to Minimax. These findings highlight the ability of ACO in decision-making algorithms without requiring knowledge of previous games. Furthermore, the results suggest that the ACO-based path optimization approach can be an effective alternative to improve the efficiency of decisions made by intelligent systems in environments that require rapid response. © 2023 IEEE. |
