{"title":"学习在第一人称射击游戏中拦截对手","authors":"Bulent Tastan, Yuan Chang, G. Sukthankar","doi":"10.1109/CIG.2012.6374144","DOIUrl":null,"url":null,"abstract":"One important aspect of creating game bots is adversarial motion planning: identifying how to move to counter possible actions made by the adversary. In this paper, we examine the problem of opponent interception, in which the goal of the bot is to reliably apprehend the opponent. We present an algorithm for motion planning that couples planning and prediction to intercept an enemy on a partially-occluded Unreal Tournament map. Human players can exhibit considerable variability in their movement preferences and do not uniformly prefer the same routes. To model this variability, we use inverse reinforcement learning to learn a player-specific motion model from sets of example traces. Opponent motion prediction is performed using a particle filter to track candidate hypotheses of the opponent's location over multiple time horizons. Our results indicate that the learned motion model has a higher tracking accuracy and yields better interception outcomes than other motion models and prediction methods.","PeriodicalId":288052,"journal":{"name":"2012 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Learning to intercept opponents in first person shooter games\",\"authors\":\"Bulent Tastan, Yuan Chang, G. Sukthankar\",\"doi\":\"10.1109/CIG.2012.6374144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One important aspect of creating game bots is adversarial motion planning: identifying how to move to counter possible actions made by the adversary. In this paper, we examine the problem of opponent interception, in which the goal of the bot is to reliably apprehend the opponent. We present an algorithm for motion planning that couples planning and prediction to intercept an enemy on a partially-occluded Unreal Tournament map. Human players can exhibit considerable variability in their movement preferences and do not uniformly prefer the same routes. To model this variability, we use inverse reinforcement learning to learn a player-specific motion model from sets of example traces. Opponent motion prediction is performed using a particle filter to track candidate hypotheses of the opponent's location over multiple time horizons. Our results indicate that the learned motion model has a higher tracking accuracy and yields better interception outcomes than other motion models and prediction methods.\",\"PeriodicalId\":288052,\"journal\":{\"name\":\"2012 IEEE Conference on Computational Intelligence and Games (CIG)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Conference on Computational Intelligence and Games (CIG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIG.2012.6374144\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Conference on Computational Intelligence and Games (CIG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIG.2012.6374144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning to intercept opponents in first person shooter games
One important aspect of creating game bots is adversarial motion planning: identifying how to move to counter possible actions made by the adversary. In this paper, we examine the problem of opponent interception, in which the goal of the bot is to reliably apprehend the opponent. We present an algorithm for motion planning that couples planning and prediction to intercept an enemy on a partially-occluded Unreal Tournament map. Human players can exhibit considerable variability in their movement preferences and do not uniformly prefer the same routes. To model this variability, we use inverse reinforcement learning to learn a player-specific motion model from sets of example traces. Opponent motion prediction is performed using a particle filter to track candidate hypotheses of the opponent's location over multiple time horizons. Our results indicate that the learned motion model has a higher tracking accuracy and yields better interception outcomes than other motion models and prediction methods.