{"title":"使用计算机视觉和机器学习技术从电脑游戏中提取NPC行为","authors":"A. Fink, J. Denzinger, John Aycock","doi":"10.1109/CIG.2007.368075","DOIUrl":null,"url":null,"abstract":"We present a first application of a general approach to learn the behavior of NPCs (and other entities) in a game from observing just the graphical output of the game during game play. This allows some understanding of what a human player might be able to learn during game play. The approach uses object tracking and situation-action pairs with the nearest-neighbor rule. For the game of Pong, we were able to predict the correct behavior of the computer controlled components approximately 9 out of 10 times, even if we keep the usage of knowledge about the game (beyond observing the images) at a minimum","PeriodicalId":365269,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Games","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Extracting NPC behavior from computer games using computer vision and machine learning techniques\",\"authors\":\"A. Fink, J. Denzinger, John Aycock\",\"doi\":\"10.1109/CIG.2007.368075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a first application of a general approach to learn the behavior of NPCs (and other entities) in a game from observing just the graphical output of the game during game play. This allows some understanding of what a human player might be able to learn during game play. The approach uses object tracking and situation-action pairs with the nearest-neighbor rule. For the game of Pong, we were able to predict the correct behavior of the computer controlled components approximately 9 out of 10 times, even if we keep the usage of knowledge about the game (beyond observing the images) at a minimum\",\"PeriodicalId\":365269,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence and Games\",\"volume\":\"136 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence and Games\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIG.2007.368075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence and Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIG.2007.368075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extracting NPC behavior from computer games using computer vision and machine learning techniques
We present a first application of a general approach to learn the behavior of NPCs (and other entities) in a game from observing just the graphical output of the game during game play. This allows some understanding of what a human player might be able to learn during game play. The approach uses object tracking and situation-action pairs with the nearest-neighbor rule. For the game of Pong, we were able to predict the correct behavior of the computer controlled components approximately 9 out of 10 times, even if we keep the usage of knowledge about the game (beyond observing the images) at a minimum