A Monte-Carlo approach for the endgame of Ms. Pac-Man

Ms. Pac-Man is a challenging video game which provides an interesting platform for artificial intelligence and computational intelligence research. This paper introduces the novel concept of path testing and reports an effective Monte-Carlo approach to develop an endgame module of an intelligent agent that plays the game. Our experimental results show that the proposed method often helps Ms. Pac-Man to eat pills effectively in the endgame. It enables the agent to advance to higher stages and earn more scores. Our agent with the endgame module has achieved a 20% increase in average score over the same agent without the module.