Optimizing neural networks for playing tic-tac-toe

A neural network approach for playing the game tic-tac-toe is introduced. The problem is considered as a combinatorial optimization problem aiming to maximize the value of a heuristic evaluation function. The proposed design guarantees a feasible solution, including in the cases where a winning move is never missed and a losing position is always prevented, if possible. The design has been implemented on a Hopfield network, a Boltzmann machine, and a Gaussian machine. The performance of the models was compared through simulation. >