2012 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第5页

Smart terrain causality chains for adventure-game puzzle generation 用于冒险游戏解谜生成的智能地形因果链

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374173

Isaac Dart, M. Nelson

引用次数: 13

Computational creativity in a closed game system 封闭游戏系统中的计算创造力

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374169

C. Browne, S. Colton

引用次数: 6

Evolving levels for Super Mario Bros using grammatical evolution 用语法进化法进化《超级马里奥兄弟》的关卡

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374170

Noor Shaker, Miguel Nicolau, Georgios N. Yannakakis, J. Togelius, M. O’Neill

引用次数: 115

Exploring optimization strategies in board game Abalone for Alpha-Beta search 探索棋盘游戏《鲍鱼》的Alpha-Beta搜索优化策略

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374139

Athanasios Papadopoulos, Konstantinos Toumpas, Anthony C. Chrysopoulos, P. Mitkas

引用次数: 12

Dynamic difficulty for checkers and Chinese chess 跳棋和中国象棋的动态难度

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374138

Laurentiu Ilici, Jiaojian Wang, Olana Missura, Thomas Gärtner

引用次数: 8

Influence Map-based controllers for Ms. PacMan and the ghosts 《吃豆女士》和幽灵的地图控制器

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374164

J. Svensson, S. Johansson

引用次数: 13

A binary classification approach for automatic preference modeling of virtual agents in Civilization IV 《文明IV》虚拟主体偏好自动建模的二元分类方法

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374151

Marlos C. Machado, G. Pappa, L. Chaimowicz

引用次数: 8

Interactively training first person shooter bots 交互式训练第一人称射击机器人

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374149

M. McPartland, M. Gallagher

引用次数: 8

The Perfect Looting System: Looking for a Phoenix? 完美的掠夺系统:寻找凤凰?

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374179

D. Maggiorini, Antonio Nigro, L. Ripamonti, M. Trubian

{"title":"The Perfect Looting System: Looking for a Phoenix?","authors":"D. Maggiorini, Antonio Nigro, L. Ripamonti, M. Trubian","doi":"10.1109/CIG.2012.6374179","DOIUrl":"https://doi.org/10.1109/CIG.2012.6374179","url":null,"abstract":"Massively Multiplayer Online (Role-Playing) Games (MMORPGs and MMOs) are highly complex socio-technical distributed systems. The thickly knitted web of relations among players is the backbone upon which their success (or failure) lies. For this reason, both game designers and managers should be aware of how - even the thinnest - features of the environment will impact on players. We can enlist among these apparently “minor” aspects Looting Systems (LSs), which help allocating valuable in-game objects, as fairly as possible, among the players. In the present work, we try to cast some light on the medium/long term effects of the adoption of different LSs on players' satisfaction (and hence on their “fidelity” to a specific game service). We adopt an agent-based approach to simulate the effects and drawbacks of LSs on different player bases (according to Bartle's player types), demonstrating that a relation among LSs and players' satisfaction with the game service could really exists, and that an appropriate choice of the LS could affect the distribution of player types in the game community, thus offering hints for reflection and further investigation to MMOs/MMORPGs developers.","PeriodicalId":288052,"journal":{"name":"2012 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132075063","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: 10

How players lose interest in playing a game: An empirical study based on distributions of total playing times 玩家如何失去对游戏的兴趣:基于总游戏时间分布的实证研究

2012 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2012-12-06 DOI: 10.1109/CIG.2012.6374148

C. Bauckhage, K. Kersting, R. Sifa, Christian Thurau, Anders Drachen, Alessandro Canossa

{"title":"How players lose interest in playing a game: An empirical study based on distributions of total playing times","authors":"C. Bauckhage, K. Kersting, R. Sifa, Christian Thurau, Anders Drachen, Alessandro Canossa","doi":"10.1109/CIG.2012.6374148","DOIUrl":"https://doi.org/10.1109/CIG.2012.6374148","url":null,"abstract":"Analyzing telemetry data of player behavior in computer games is a topic of increasing interest for industry and research, alike. When applied to game telemetry data, pattern recognition and statistical analysis provide valuable business intelligence tools for game development. An important problem in this area is to characterize how player engagement in a game evolves over time. Reliable models are of pivotal interest since they allow for assessing the long-term success of game products and can provide estimates of how long players may be expected to keep actively playing a game. In this paper, we introduce methods from random process theory into game data mining in order to draw inferences about player engagement. Given large samples (over 250,000 players) of behavioral telemetry data from five different action-adventure and shooter games, we extract information as to how long individual players have played these games and apply techniques from lifetime analysis to identify common patterns. In all five cases, we find that the Weibull distribution gives a good account of the statistics of total playing times. This implies that an average player's interest in playing one of the games considered evolves according to a non-homogeneous Poisson process. Therefore, given data on the initial playtime behavior of the players of a game, it becomes possible to predict when they stop playing.","PeriodicalId":288052,"journal":{"name":"2012 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"46 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120887448","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: 98