2016 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第7页

Computational intelligence and cognitive performance assessment games 计算智能和认知表现评估游戏

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-02 DOI: 10.1109/CIG.2016.7860388

Christoffer Holmgård, J. Togelius, L. Henriksen

{"title":"Computational intelligence and cognitive performance assessment games","authors":"Christoffer Holmgård, J. Togelius, L. Henriksen","doi":"10.1109/CIG.2016.7860388","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860388","url":null,"abstract":"In this paper, we present the idea that game design, player modeling, and procedural content generation may offer new methods for modern psychological assessment, allowing for daily cognitive assessment in ways previously unseen. We suggest that games often share properties with psychological tests and that the overlap between the two domains might allow for creating games that contain assessment elements and provide examples from the literature that already show this. While approaches like these are typically seen as adding noise to a particular instrument in a psychometric context, research in player modeling demonstrates that it is possible to extract reliable measures corresponding to psychological constructs from in-game behavior and performance. Given these observations, we suggest that the combination of game design, player modeling, and procedural content generation offers new opportunities for conducting psychometric testing with a higher frequency and a higher degree of personalization than has previously been possible. Finally, we describe how we are currently implementing the first version of this vision in the form of an application for mobile devices that will soon be used in upcoming user studies.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"1 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89856285","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: 9

General general game AI 通用通用游戏AI

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-02 DOI: 10.1109/CIG.2016.7860385

J. Togelius, Georgios N. Yannakakis

引用次数: 19

Generating heuristics for novice players 为新手玩家生成启发式信息

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-02 DOI: 10.1109/CIG.2016.7860407

F. Silva, Aaron Isaksen, J. Togelius, Andy Nealen

引用次数: 23

Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game 预测玩家流失率:预测大型在线游戏中玩家离开的隐马尔可夫模型方法

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-02 DOI: 10.1109/CIG.2016.7860431

M. Tamassia, W. Raffe, R. Sifa, Anders Drachen, Fabio Zambetta, M. Hitchens

引用次数: 36

Guns and guardians: Comparative cluster analysis and behavioral profiling in destiny 枪支和监护人:命运中的比较聚类分析和行为分析

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-02 DOI: 10.1109/CIG.2016.7860423

Anders Drachen, James Green, Chester Gray, Elie Harik, Patty Lu, R. Sifa, D. Klabjan

{"title":"Guns and guardians: Comparative cluster analysis and behavioral profiling in destiny","authors":"Anders Drachen, James Green, Chester Gray, Elie Harik, Patty Lu, R. Sifa, D. Klabjan","doi":"10.1109/CIG.2016.7860423","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860423","url":null,"abstract":"Behavioral profiling in digital games with persistent online worlds are vital for a variety of tasks ranging from understanding the player community to informing design and business decisions. In this paper behavioral profiles are developed for the online multiplayer shooter/role-playing game Destiny, the most expensive game to be launched to date and a unique hybrid incorporating designs from multiple traditional genres. The profiles are based on playstyle features covering a total of 41 features and over 4,800 randomly selected players at the highest level in the game. Four clustering models were applied (k-means, Gaussian mixture models, k-maxoids and Archetype Analysis) across the two primary game modes in Destiny: Player-versus-Player and Player-versus-Environment. The performance of each model is described and cross-model analysis is used to identify four to five distinct playstyles across each method, using a variety of similarity metrics. Discussion on which model to use in different circumstances is provided. The profiles are translated into design language and the insights they provide into the behavior of Destiny's player base described.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"140 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89918466","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: 24

Hyper-heuristic general video game playing 超启发式一般电子游戏

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-02 DOI: 10.1109/CIG.2016.7860398

André Mendes, J. Togelius, Andy Nealen

引用次数: 36

Towards the automatic optimisation of procedural content generators 面向程序内容生成器的自动优化

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-06-14 DOI: 10.1109/CIG.2016.7860424

Michael Cook, J. Gow, S. Colton

引用次数: 6

ViZDoom: A Doom-based AI research platform for visual reinforcement learning ViZDoom:基于doom的人工智能研究平台，用于视觉强化学习

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-05-06 DOI: 10.1109/CIG.2016.7860433

Michal Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, Wojciech Jaśkowski

{"title":"ViZDoom: A Doom-based AI research platform for visual reinforcement learning","authors":"Michal Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, Wojciech Jaśkowski","doi":"10.1109/CIG.2016.7860433","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860433","url":null,"abstract":"The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective. Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world. The software, called ViZDoom, is based on the classical first-person shooter video game, Doom. It allows developing bots that play the game using the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a convenient mechanism of user scenarios. In the experimental part, we test the environment by trying to learn bots for two scenarios: a basic move-and-shoot task and a more complex maze-navigation problem. Using convolutional deep neural networks with Q-learning and experience replay, for both scenarios, we were able to train competent bots, which exhibit human-like behaviors. The results confirm the utility of ViZDoom as an AI research platform and imply that visual reinforcement learning in 3D realistic first-person perspective environments is feasible.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"97 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85752075","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: 625

Keynote speech IV: Where games meet hyper-heuristics 主题演讲四:游戏与超启发式的结合

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317660

G. Kendall

引用次数: 0

Keynote speech I: Co-evolutionary learning in game-playing 主题演讲1:游戏中的共同进化学习

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317657

X. Yao

{"title":"Keynote speech I: Co-evolutionary learning in game-playing","authors":"X. Yao","doi":"10.1109/CIG.2015.7317657","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317657","url":null,"abstract":"Co-evolution has been used widely in automatic learning of game-playing strategies, e.g., for iterated prisoner's dilemma games, backgammon, chess, etc. It is a very interesting form of learning because it learns by interactions only, without any explicit target output information. In other words, the correct choices or moves were not provided as teacher information in learning. Yet co-evolutionary learning is still able to learn high-performance, in comparison to average human performance, game-playing strategies. Interestingly, the research of co-evolutionary learning has not focused on its generalisation ability, in sharp contrast to machine learning in general, where generalisation is at the heart of learning of any form. This talk presents one of the few generic frameworks that are available for measuring generalisation of coevolutionary learning. It enables us to discuss and study generalisation of different co-evolutionary algorithms more objectively and quantitatively. As a result, it enables us to draw more appropriate conclusions about the abilities of our learned game-playing strategies in dealing with totally new and unseens environments (including opponents). The iterated prisoner's dilemma game will be used as an example in this talk to illustrate our theoretical framework and performance improvements we could gain by following this more principled approach to co-evolutionary learning.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"52 360 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83736200","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: 0