A hybrid method of Dijkstra algorithm and evolutionary neural network for optimal Ms. Pac-Man agent

Many researchers have interest on an auto-play game agent for Ms. Pac-Man, a classical real-time arcade game, using artificial intelligence. In order to control Ms. Pac-Man two ways are used. One is human-designed rules and the other is using evolutionary computation. Though well-defined rules, that use commonly search algorithms, guarantee stable high score, unpredicted situations can be happened because it is hard to consider every case. Evolutionary computation helps making a controller that covers uncertain circumstances that human do not think. These two methods can support each other. This paper proposes a hybrid method to design a controller to automatically play Ms. Pac-Man based on hand-coded rules and evolutionary computation. Rules are based on Dijkstra algorithms. In order to cover rules, evolutionary artificial neural networks are used. We have confirmed that the controller using the method makes higher performance than using each method separately by comparing with points of each other after playing game.