Uniform versus Gaussian mutators in automatic generation of game AI in Ms. Pac-man using hill-climbing

This paper explores the idea of combining the hill-climbing concept into feed-forward artificial neural networks (ANN) to develop intelligent controllers to play the Ms. Pacman game. The resulting algorithm is referred to as the HillClimbingNet. A comparison with a random system, called RandNet is conducted on the same problem. We also present a survey of the effects of two most popular probability density functions, uniform and Gaussian distributions/mutators on the introduced algorithm. The results clearly indicate the strong potential of the hill-climbing strategy as a direct search method in tandem with a Gaussian-based mutator to optimize the ANN for playing Ms. Pac-Man.