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arXiv · 1309.1543

A Comparism of the Performance of Supervised and Unsupervised Machine Learning Techniques in evolving Awale/Mancala/Ayo Game Player

O. A. Randle·O. O. Ogunduyile·T. Zuva·N. A. Fashola
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Abstract

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.

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O. A. Randle, O. O. Ogunduyile, T. Zuva, N. A. Fashola. 2013-09-06. A Comparism of the Performance of Supervised and Unsupervised Machine Learning Techniques in evolving Awale/Mancala/Ayo Game Player. https://arxiv.org/abs/1309.1543

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