2016 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第5页

Position-based reinforcement learning biased MCTS for General Video Game Playing 基于位置的强化学习偏向MCTS的一般视频游戏

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860449

C. Chu, Suguru Ito, Tomohiro Harada, R. Thawonmas

引用次数: 5

Recovering visibility and dodging obstacles in pursuit-evasion games 在追逐逃避游戏中恢复能见度和躲避障碍物

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860419

Ahmed Abdelkader

引用次数: 0

Evaluating real-time strategy game states using convolutional neural networks 使用卷积神经网络评估实时策略游戏状态

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860439

Marius Stanescu, Nicolas A. Barriga, Andy Hess, M. Buro

{"title":"Evaluating real-time strategy game states using convolutional neural networks","authors":"Marius Stanescu, Nicolas A. Barriga, Andy Hess, M. Buro","doi":"10.1109/CIG.2016.7860439","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860439","url":null,"abstract":"Real-time strategy (RTS) games, such as Blizzard's StarCraft, are fast paced war simulation games in which players have to manage economies, control many dozens of units, and deal with uncertainty about opposing unit locations in real-time. Even in perfect information settings, constructing strong AI systems has been difficult due to enormous state and action spaces and the lack of good state evaluation functions and high-level action abstractions. To this day, good human players are still handily defeating the best RTS game AI systems, but this may change in the near future given the recent success of deep convolutional neural networks (CNNs) in computer Go, which demonstrated how networks can be used for evaluating complex game states accurately and to focus look-ahead search. In this paper we present a CNN for RTS game state evaluation that goes beyond commonly used material based evaluations by also taking spatial relations between units into account. We evaluate the CNN's performance by comparing it with various other evaluation functions by means of tournaments played by several state-of-the-art search algorithms. We find that, despite its much slower evaluation speed, on average the CNN based search performs significantly better compared to simpler but faster evaluations. These promising initial results together with recent advances in hierarchical search suggest that dominating human players in RTS games may not be far off.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"35 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77467518","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: 46

Monte-Carlo simulation balancing revisited 蒙特卡罗模拟平衡重新审视

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860411

Tobias Graf, M. Platzner

引用次数: 4

Heuristics for sleep and heal in combat 启发式睡眠和治疗在战斗中

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860401

Shuo Xu, Clark Verbrugge

引用次数: 1

Personalised track design in car racing games 赛车游戏中的个性化赛道设计

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860435

Theodosis Georgiou, Y. Demiris

{"title":"Personalised track design in car racing games","authors":"Theodosis Georgiou, Y. Demiris","doi":"10.1109/CIG.2016.7860435","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860435","url":null,"abstract":"Real-time adaptation of computer games' content to the users' skills and abilities can enhance the player's engagement and immersion. Understanding of the user's potential while playing is of high importance in order to allow the successful procedural generation of user-tailored content. We investigate how player models can be created in car racing games. Our user model uses a combination of data from unobtrusive sensors, while the user is playing a car racing simulator. It extracts features through machine learning techniques, which are then used to comprehend the user's gameplay, by utilising the educational theoretical frameworks of the Concept of Flow and Zone of Proximal Development. The end result is to provide at a next stage a new track that fits to the user needs, which aids both the training of the driver and their engagement in the game. In order to validate that the system is designing personalised tracks, we associated the average performance from 41 users that played the game, with the difficulty factor of the generated track. In addition, the variation in paths of the implemented tracks between users provides a good indicator for the suitability of the system.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"49 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83368588","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: 4

An integrated process for game balancing 游戏平衡的综合过程

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860425

Marlene Beyer, Aleksandr Agureikin, Alexander Anokhin, Christoph Laenger, Felix Nolte, Jonas Winterberg, Marcel Renka, Martin Rieger, Nicolas Pflanzl, M. Preuss, Vanessa Volz

引用次数: 12

Biometrics and classifier fusion to predict the fun-factor in video gaming 生物识别和分类器融合预测电子游戏中的乐趣因素

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860418

Andrea Clerico, Cindy Chamberland, Mark Parent, P. Michon, S. Tremblay, T. Falk, Jean-Christophe Gagnon, P. Jackson

{"title":"Biometrics and classifier fusion to predict the fun-factor in video gaming","authors":"Andrea Clerico, Cindy Chamberland, Mark Parent, P. Michon, S. Tremblay, T. Falk, Jean-Christophe Gagnon, P. Jackson","doi":"10.1109/CIG.2016.7860418","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860418","url":null,"abstract":"The key to the development of adaptive gameplay is the capability to monitor and predict in real time the players experience (or, herein, fun factor). To achieve this goal, we rely on biometrics and machine learning algorithms to capture a physiological signature that reflects the player's affective state during the game. In this paper, we report research and development effort into the real time monitoring of the player's level of fun during a commercially available video game session using physiological signals. The use of a triple-classifier system allows the transformation of players' physiological responses and their fluctuation into a single yet multifaceted measure of fun, using a non-linear gameplay. Our results suggest that cardiac and respiratory activities provide the best predictive power. Moreover, the level of performance reached when classifying the level of fun (70% accuracy) shows that the use of machine learning approaches with physiological measures can contribute to predicting players experience in an objective manner.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"1 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82107463","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: 20

Hierarchical Task Network Plan Reuse for video games 电子游戏的分层任务网络计划重用

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860395

Dennis J. N. J. Soemers, M. Winands

引用次数: 5

Altruistic punishment can help resolve tragedy of the commons social dilemmas 利他惩罚有助于解决公地悲剧的社会困境

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860402

G. Greenwood

引用次数: 3