{"title":"Co-Evolving Influence Map Tree Based Strategy Game Players","authors":"C. Miles, J. Quiroz, Ryan E. Leigh, S. Louis","doi":"10.1109/CIG.2007.368083","DOIUrl":null,"url":null,"abstract":"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","PeriodicalId":365269,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Games","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"46","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence and Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIG.2007.368083","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 46
Abstract
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