Ensemble decision making in real-time games

Philip Rodgers, John Levine, Damien Anderson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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)978-1-5386-4359-4
ISBN (Print)978-1-5386-4360-0
DOIs
Publication statusPublished - 15 Oct 2018

Publication series

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

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Decision making

Keywords

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

Cite this

Rodgers, P., Levine, J., & Anderson, D. (2018). Ensemble decision making in real-time games. In 2018 IEEE Conference on Computational Intelligence and Games (CIG) IEEE. https://doi.org/10.1109/CIG.2018.8490401
Rodgers, Philip ; Levine, John ; Anderson, Damien. / Ensemble decision making in real-time games. 2018 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2018.
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Rodgers, P, Levine, J & Anderson, D 2018, Ensemble decision making in real-time games. in 2018 IEEE Conference on Computational Intelligence and Games (CIG). IEEE. https://doi.org/10.1109/CIG.2018.8490401

Ensemble decision making in real-time games. / Rodgers, Philip; Levine, John; Anderson, Damien.

2018 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Ensemble decision making in real-time games

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AU - Anderson, Damien

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AB - 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.

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KW - decision

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Rodgers P, Levine J, Anderson D. Ensemble decision making in real-time games. In 2018 IEEE Conference on Computational Intelligence and Games (CIG). IEEE. 2018 https://doi.org/10.1109/CIG.2018.8490401

Documents and Links

  • Rodgers, P. et al (2018) Ensemble decision making in real-time games

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