Video games can be used as an excellent test bed for Artificial Intelligence (AI) techniques. They are challenging and non-deterministic, this makes it very difficult to write strong AI players. An example of such a video game is Ms. Pac-Man. In this paper we will outline some of the previous techniques used to build AI controllers for Ms. Pac-Man as well as presenting a new and novel solution. My technique utilises a Cellular Automaton (CA) to build a representation of the environment at each time step of the game. The basis of the representation is a 2-D grid of cells. Each cell has a state and a set of rules which determine whether or not that cell will dominate (i.e. pass its state value onto) adjacent cells at each update. Once a certain number of update iterations have been completed, the CA represents the state of the environment in the game, the goals and the dangers. At this point, Ms. Pac-Man will decide her next move based only on her adjacent cells, that is to say, she has no knowledge of the state of the environment as a whole, she will simply follow the strongest path. This technique shows promise and allows the controller to achieve high scores in a live game, the behaviour it exhibits is interesting and complex. Moreover, this behaviour is produced by using very simple rules which are applied many times to each cell in the grid. Simple local interactions with complex global results are truly achieved.