Co-evolving influence map tree based strategy game players

Chris Miles*, Juan Quiroz, Ryan Leigh, Sushil J. Louis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

38 Citations (Scopus)


We investigate the use of genetic algorithms to evolve AI players for real-time strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we evolve players through co-evolution. Our game players are implemented as resource allocation systems. Influence map trees are used to analyze the game-state and determine promising places to attack, defend, etc. These spatial objectives are chained to non-spatial objectives (train units, build buildings, gather resources) in a dependency graph. Players are encoded within the individuals of a genetic algorithm and co-evolved against each other, with results showing the production of strategies that are innovative, robust, and capable of defeating a suite of hand-coded opponents.

Original languageEnglish
Title of host publication2007 IEEE Symposium on Computational Intelligence and Games
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781424407095
Publication statusPublished - 2007
Externally publishedYes
EventIEEE Symposium on Computational Intelligence and Games - Honolulu
Duration: 1 Apr 20075 Apr 2007


ConferenceIEEE Symposium on Computational Intelligence and Games


  • co-evolution
  • game AI
  • computer game
  • real-time strategy games


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