A Comparism of the Performance of Supervised and Unsupervised Machine Learning Techniques in evolving Awale/Mancala/Ayo Game Player
This work addresses game AI development for Awale, but it appears incremental as it focuses on comparing existing techniques without introducing new methods.
The paper compared supervised and unsupervised machine learning techniques for developing Awale game players, evaluating performance using methods like minimax and endgame databases to identify the best approaches.
Awale games have become widely recognized across the world, for their innovative strategies and techniques which were used in evolving the agents (player) and have produced interesting results under various conditions. This paper will compare the results of the two major machine learning techniques by reviewing their performance when using minimax, endgame database, a combination of both techniques or other techniques, and will determine which are the best techniques.