A Model-Based Approach to Optimizing Ms. Pac-Man Game Strategies in Real Time
Greg Foderaro, Ashleigh Swingler, Silvio Ferrari · IEEE Transactions on Computational Intelligence and AI in Games · 2016
This paper presents a model-based approach for computing real-time optimal decision strategies in the pursuit-evasion game of Ms. Pac-Man. The game of Ms. Pac-Man is an excellent benchmark problem of pursuit-evasion game with multiple, active adversaries that adapt their pursuit policies based on Ms. Pac-Man's state and decisions. In addition to evading the adversaries, the agent must pursue multiple fixed and moving targets in an obstacle-populated environment. This paper presents a novel approach by which a decision-tree representation of all possible strategies is derived from the maze geometry and the dynamic equations of the adversaries or ghosts. The proposed models of ghost dynamics and decisions are validated through extensive numerical simulations. During the game, the decision tree is updated and used to determine optimal strategies in real time based on state estimates and game predictions obtained iteratively over time. The results show that the artificial player obtained by this approach is able to achieve high game scores, and to handle high game levels in which the characters speeds and maze complexity become challenging even for human players.