Evolving a Neural Network Location Evaluator to Play Ms. Pac-Man

Ms. Pac-Man is a challenging, classic arcade game with a certain cult status. This paper reports attempts to evolve a Pac-Man player, where the control algorithm uses a neural network to evaluate the possible next moves. The evolved neural network takes a handcrafted feature vector based on a candidate maze location as input, and produces a score for that location as output. Results are reported on two simulated versions of the game: deterministic and nondeterministic. The results show that useful behaviours can be evolved that are frequently capable of clearing the first level, but are still susceptible to making poor decisions. Currently, the best evolved players play at the level of a reasonable human novice.

• Type: Conference
• Year: 2005
• Where: CIG
• Authors: Simon M. Lucas
• Keywords: Certain Cult Status, CIG 2005, Evolved Neural Network, Neural Network