2007 IEEE Symposium on Computational Intelligence and Games最新文献_第5页

Hybrid Evolutionary Learning Approaches for The Virus Game 病毒游戏的混合进化学习方法

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368098

M. Naveed, P. Cowling, M. A. Hossain

引用次数: 4

Waving Real Hand Gestures Recorded by Wearable Motion Sensors to a Virtual Car and Driver in a Mixed-Reality Parking Game 在混合现实停车游戏中，可穿戴运动传感器向虚拟汽车和驾驶员挥动真实手势

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368076

D. Bannach, O. Amft, K. Kunze, E. A. Heinz, G. Tröster, P. Lukowicz

引用次数: 45

Bayesian Opponent Modeling in a Simple Poker Environment 简单扑克环境中的贝叶斯对手建模

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368088

Roderick J. S. Baker, P. Cowling

引用次数: 19

Extracting NPC behavior from computer games using computer vision and machine learning techniques 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

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368075

A. Fink, J. Denzinger, John Aycock

引用次数: 13

EvoTanks: Co-Evolutionary Development of Game-Playing Agents EvoTanks:游戏代理的共同进化发展

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368116

Thomas Thompson, J. Levine, G. Hayes

引用次数: 9

Coevolving Strategies for General Game Playing 一般博弈的共同进化策略

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368115

J. Reisinger, E. Bahçeci, Igor Karpov, R. Miikkulainen

引用次数: 39

Computer Strategies for Solitaire Yahtzee 纸牌Yahtzee的计算机策略

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368089

James R. Glenn

引用次数: 12

Automatic Generation of Evaluation Features for Computer Game Players 计算机游戏玩家评价功能的自动生成

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368108

Makoto Miwa, Daisaku Yokoyama, T. Chikayama

{"title":"Automatic Generation of Evaluation Features for Computer Game Players","authors":"Makoto Miwa, Daisaku Yokoyama, T. Chikayama","doi":"10.1109/CIG.2007.368108","DOIUrl":"https://doi.org/10.1109/CIG.2007.368108","url":null,"abstract":"Accuracy of evaluation functions is one of the critical factors in computer game players. Evaluation functions are usually constructed manually as a weighted linear combination of evaluation features that characterize game positions. Selecting evaluation features and tuning their weights require deep knowledge of the game and largely alleviates such efforts. In this paper, we propose a new fast and scalable method to automatically generate game position features based on game records to be used in evaluation functions. Our method treats two-class problems which is widely applicable to many types of games. Evaluation features are built as conjunctions of the simplest features representing positions. We select these features based on two measures: frequency and conditional mutual information. To evaluate the proposed method, we applied it to 200,000 Othello positions. The proposed selection method is found to be effective, showing much better results than when simple features are used. The naive Bayesian classifier using automatically generated features showed the accuracy close to 80% in win/lose classification. We also show that this generation method can be parallelized easily and can treat large scale problems by converting these selection algorithms into incremental selection algorithms","PeriodicalId":365269,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Games","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122493728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Fuzzy Prolog as Cognitive Layer in RoboCupSoccer 模糊Prolog作为机器人足球的认知层

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368118

S. Muñoz-Hernández, W. S. Wiguna

{"title":"Fuzzy Prolog as Cognitive Layer in RoboCupSoccer","authors":"S. Muñoz-Hernández, W. S. Wiguna","doi":"10.1109/CIG.2007.368118","DOIUrl":"https://doi.org/10.1109/CIG.2007.368118","url":null,"abstract":"RoboCupSoccer domain has several leagues which varies in the rule of play such as specification of players, number of players, field size, and time length. Nevertheless, each RoboCup league is a variant of a soccer league and therefore they are based on some basic rules of soccer. A layered design of agents system presented in the work of Garcia et al. (2004) shows a modular approach to build control for a team of robots participating in RoboCupSoccer E-League. Based on this design, we propose a generalized architecture offering flexibility to switch between leagues and programming language while maintaining Prolog as cognitive layer. Prolog is a very convenient tool to design strategies for soccer players using simple rules close to human reasoning. Sometimes this reasoning needs to deal with uncertainty, fuzziness or incompleteness of the information. In these cases it is useful Fuzzy Prolog (Guadarrama et al., 2004), (Munoz-Hernandez and Vaucheret, 2005), (Munoz-Hernandez and Gomez-Perez, 2005), (Munoz-Hernandez and Vaucheret, 2006). In this paper we propose to use a combination of Prolog (that is crisp) and Fuzzy Prolog to implement the cognitive layer in RoboCupSoccer, which has the advantage of incorporating as conventional logic as fuzzy logic in this layer. A prototype of a team based on this architecture has been build for RoboCup soccer simulator, and we show that this approach provides a convenient way of incorporating a team strategy in high level (human-like) manner, where technical details are encapsulated and fuzzy information is represented","PeriodicalId":365269,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Games","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127281872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Co-Evolving Influence Map Tree Based Strategy Game Players 共同进化影响基于地图树的策略游戏玩家

2007 IEEE Symposium on Computational Intelligence and Games Pub Date : 2007-04-01 DOI: 10.1109/CIG.2007.368083

C. Miles, J. Quiroz, Ryan E. Leigh, S. Louis

引用次数: 46