Using a novel approach for computing real-time game strategy, engineers have developed an artificial Ms. Pac-Man player that chomps the existing high score for computerized play.
In the popular arcade game, Ms. Pac-Man must evade ghost enemies while she collects items and navigates an obstacle-populated maze. The game is somewhat of a favorite among engineers and computer scientists who compete to see who can program the best artificial player.
Using a novel approach for computing real-time game strategy, engineers have developed an artificial Ms. Pac-Man player that chomps the existing high score for computerized play.
In the popular arcade game, Ms. Pac-Man must evade ghost enemies while she collects items and navigates an obstacle-populated maze. The game is somewhat of a favorite among engineers and computer scientists who compete to see who can program the best artificial player.
The record score at the annual Ms. Pac-Man Screen Capture Competition stands at 36,280, but a trio of researchers led by Silvia Ferrari, professor of mechanical and aerospace engineering at Cornell, has produced a laboratory score of 43,720.
The score was achieved using a decision-tree approach in which the optimal moves for the artificial player are derived from a maze of geometry and dynamic equations that predict the movements of the ghosts with 94.6-percent accuracy. As the game progresses, the decision tree is updated in real-time. The strategy is detailed in the study “A Model-Based Approach to Optimizing Ms. Pac-Man Game Strategies in Real Time,” to be published by the journal IEEE Transactions on Computational Intelligence and AI in Games.
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Photo credit: Rob Kurcoba
