Ensemble decision making in real-time games

Philip Rodgers, John Levine, Damien Anderson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

77 Downloads (Pure)


This paper describes an Ensemble Agent for the classic arcade game Ms. Pac-Man. Our approach decomposes the problem into sub-goals. An expert agent is created for each sub-goal, with all experts reporting to a central arbiter. Our Ensemble Agent has achieved the AI world record for the arcade version of Ms. Pac-Man with a score of 162,280. For comparison, a MCTS-based monolithic agent was also created, based on the same accurate forward model that the Ensemble Agent uses, reaching a score of 115,180.
Original languageEnglish
Title of host publication2018 IEEE Conference on Computational Intelligence and Games (CIG)
Number of pages8
ISBN (Electronic)978-1-5386-4359-4
ISBN (Print)978-1-5386-4360-0
Publication statusPublished - 15 Oct 2018

Publication series

ISSN (Print)2325-4270
ISSN (Electronic)2325-4289


  • ensemble
  • mcts
  • Pac-Man
  • real-time
  • decision


Dive into the research topics of 'Ensemble decision making in real-time games'. Together they form a unique fingerprint.

Cite this