游戏内容和AI共同进化的竞争算法案例研究:《星球大战》

Q2 Computer Science
M. Nogueira, C. Cotta, Antonio J. Fernández
{"title":"游戏内容和AI共同进化的竞争算法案例研究:《星球大战》","authors":"M. Nogueira, C. Cotta, Antonio J. Fernández","doi":"10.1109/TCIAIG.2015.2499281","DOIUrl":null,"url":null,"abstract":"The classical approach of Competitive Coevolution (CC) applied in games tries to exploit an arms race between coevolving populations that belong to the same species (or at least to the same biotic niche), namely strategies, rules, tracks for racing, or any other. This paper proposes the co-evolution of entities belonging to different realms (namely biotic and abiotic) via a competitive approach. More precisely, we aim to coevolutionarily optimize both virtual players and game content. From a general perspective, our proposal can be viewed as a method of procedural content generation combined with a technique for generating game Artificial Intelligence (AI). This approach can not only help game designers in game creation but also generate content personalized to both specific players’ profiles and game designer’s objectives (e.g., create content that favors novice players over skillful players). As a case study we use Planet Wars, the Real Time Strategy (RTS) game associated with the 2010 Google AI Challenge contest, and demonstrate (via an empirical study) the validity of our approach.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"325-337"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2499281","citationCount":"0","resultStr":"{\"title\":\"Competitive Algorithms for Coevolving Both Game Content and AI. A Case Study: Planet Wars\",\"authors\":\"M. Nogueira, C. Cotta, Antonio J. Fernández\",\"doi\":\"10.1109/TCIAIG.2015.2499281\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The classical approach of Competitive Coevolution (CC) applied in games tries to exploit an arms race between coevolving populations that belong to the same species (or at least to the same biotic niche), namely strategies, rules, tracks for racing, or any other. This paper proposes the co-evolution of entities belonging to different realms (namely biotic and abiotic) via a competitive approach. More precisely, we aim to coevolutionarily optimize both virtual players and game content. From a general perspective, our proposal can be viewed as a method of procedural content generation combined with a technique for generating game Artificial Intelligence (AI). This approach can not only help game designers in game creation but also generate content personalized to both specific players’ profiles and game designer’s objectives (e.g., create content that favors novice players over skillful players). As a case study we use Planet Wars, the Real Time Strategy (RTS) game associated with the 2010 Google AI Challenge contest, and demonstrate (via an empirical study) the validity of our approach.\",\"PeriodicalId\":49192,\"journal\":{\"name\":\"IEEE Transactions on Computational Intelligence and AI in Games\",\"volume\":\"8 1\",\"pages\":\"325-337\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2499281\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Intelligence and AI in Games\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TCIAIG.2015.2499281\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Intelligence and AI in Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TCIAIG.2015.2499281","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

摘要

在游戏中应用的竞争性共同进化(CC)的经典方法试图利用属于同一物种(或至少属于同一生物生态位)的共同进化群体之间的军备竞赛,即策略,规则,赛道或其他任何东西。本文提出了不同领域(即生物和非生物)的实体通过竞争的方式共同进化。更准确地说,我们的目标是共同进化优化虚拟玩家和游戏内容。从一般角度来看,我们的建议可以被视为一种程序内容生成方法与生成游戏人工智能(AI)的技术相结合。这种方法不仅可以帮助游戏设计师进行游戏创作,还可以根据特定玩家的个人资料和游戏设计师的目标生成个性化的内容(游戏邦注:例如,比起熟练玩家,创造更有利于新手玩家的内容)。作为案例研究,我们使用了《星球大战》,这是一款与2010 b谷歌AI挑战赛相关的即时战略(RTS)游戏,并通过实证研究证明了我们方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Competitive Algorithms for Coevolving Both Game Content and AI. A Case Study: Planet Wars
The classical approach of Competitive Coevolution (CC) applied in games tries to exploit an arms race between coevolving populations that belong to the same species (or at least to the same biotic niche), namely strategies, rules, tracks for racing, or any other. This paper proposes the co-evolution of entities belonging to different realms (namely biotic and abiotic) via a competitive approach. More precisely, we aim to coevolutionarily optimize both virtual players and game content. From a general perspective, our proposal can be viewed as a method of procedural content generation combined with a technique for generating game Artificial Intelligence (AI). This approach can not only help game designers in game creation but also generate content personalized to both specific players’ profiles and game designer’s objectives (e.g., create content that favors novice players over skillful players). As a case study we use Planet Wars, the Real Time Strategy (RTS) game associated with the 2010 Google AI Challenge contest, and demonstrate (via an empirical study) the validity of our approach.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信