Ensemble decision making in real-time games

Philip Rodgers, John Levine, Damien Anderson

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

2 Citations (Scopus)
156 Downloads (Pure)

Abstract

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)
PublisherIEEE
Number of pages8
ISBN (Electronic)9781538643594
ISBN (Print)9781538643600
DOIs
Publication statusPublished - 14 Oct 2018

Publication series

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

Keywords

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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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