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Teaching a Neural Network to Play Konane

A common approach to game playing in Artificial Intelligence involves the use of the Minimax algorithm and a static evaluation function. In this work I investigated using neural networks to replace hand-tuned static evaluation functions. Networks were also trained to evaluate board positions to greater depth levels using Minimax. The networks begin as a random networks and by playing against a random player, the networks are able to match the teacher’s performance. This work provides evidence that board representation affects the ability of the network to evaluate Konane board positions. Networks that are taught to output a real-value board evaluation outperform those taught to directly compare two boards after a considerable amount of training. However, the latter networks show more consistent behavior during training, and quickly learn to play at a reasonably good skill level.