An influence map model for playing Ms. Pac-Man

In this paper we develop a Ms. Pac-Man playing agent based on an influence map model. The proposed model is as simple as possible while capturing the essentials of the game. Our model has three main parameters that have an intuitive relationship to the agent's behavior. Experimental results are presented exploring the model's performance over its parameter space using random and systematic global exploration and a greedy algorithm. The model parameters can be optimized without difficulty despite the noisy fitness function used. The performance of the optimized agents is comparable to the best published results for a Ms. Pac-Man playing agent. Nevertheless, some difficulties were observed in terms of the model and the software system.