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The Upper Confidence for Trees (UCT) algorithm has been shown to perform well in complex games, but samples undesirable areas of the search space when building its game tree. This paper explores the design and parametric considerations for augmenting the UCT algorithm with an Artificial Neural Network (NN) to dynamically prune the game tree created, thus limiting the game tree created. The expansion phase of UCT is augmented with a trained NN to create a novel UCT-NN variant that includes prior knowledge and strategy. This paper considers the game of Go-Moku for investigating the design and parametric considerations of UCT-NN. The parameters considered are the exploration and exploitation balancing C parameter, the NN training and structural design parameters and the various pruning schemes which could be used in UCT-NN. Parameter tuning techniques are provided for managing the parametric concerns in the proposed algorithm. Results of parameter experiments indicate that a single value of C = 1.41 is suitable for the games studied. Suitable values were found for the structural and training parameters of NN, which were required to test various pruning schemes. Of the various pruning schemes considered, an exponentially decaying scheme is found to be superior in the UCT-NN algorithm where a large amount of moves are initially pruned, but fewer moves on deeper ply.