IEEE Transactions on Games最新文献

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-06-17 DOI: 10.1109/TG.2025.3575879

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IEEE Transactions on Games Publication Information IEEE游戏出版信息汇刊

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-06-17 DOI: 10.1109/TG.2025.3575877

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IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-03-18 DOI: 10.1109/TG.2025.3546830

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IEEE Transactions on Games Publication Information IEEE游戏出版信息汇刊

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-03-18 DOI: 10.1109/TG.2025.3546832

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From Pixels to Titles: Video Game Identification by Screenshots Using Convolutional Neural Networks 从像素到标题：使用卷积神经网络通过截图识别电子游戏

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-01-10 DOI: 10.1109/TG.2025.3528187

Fabricio Breve

{"title":"From Pixels to Titles: Video Game Identification by Screenshots Using Convolutional Neural Networks","authors":"Fabricio Breve","doi":"10.1109/TG.2025.3528187","DOIUrl":"https://doi.org/10.1109/TG.2025.3528187","url":null,"abstract":"In this article, we investigate video game identification through single screenshots, utilizing ten convolutional neural network (CNN) architectures (VGG16, ResNet50, ResNet152, MobileNet, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB2, EfficientNetB3, and EfficientNetV2S) and three transformers architectures (ViT-B16, ViT-L32, and SwinT) across 22 home console systems, spanning from Atari 2600 to PlayStation 5, totalling 8796 games and 170 881 screenshots. Except for VGG16, all CNNs outperformed the transformers in this task. Using ImageNet pretrained weights as initial weights, EfficientNetV2S achieves the highest average accuracy (77.44%) and the highest accuracy in 16 of the 22 systems. DenseNet201 is the best in four systems and EfficientNetB3 is the best in the remaining two systems. Employing alternative initial weights fine-tuned in an arcade screenshots dataset boosts accuracy for EfficientNet architectures, with the EfficientNetV2S reaching a peak accuracy of 77.63% and demonstrating reduced convergence epochs from 26.9 to 24.5 on average. Overall, the combination of optimal architecture and weights attains 78.79% accuracy, primarily led by EfficientNetV2S in 15 systems. These findings underscore the efficacy of CNNs in video game identification through screenshots.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"536-544"},"PeriodicalIF":1.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Enhancing AI-Bot Strength and Strategy Diversity in Adversarial Games: A Novel Deep Reinforcement Learning Framework 增强AI-Bot在对抗游戏中的力量和策略多样性：一种新的深度强化学习框架

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-12-23 DOI: 10.1109/TG.2024.3520970

Chenglu Sun;Shuo Shen;Deyi Xue;Wenzhi Tao;Zixia Zhou

{"title":"Enhancing AI-Bot Strength and Strategy Diversity in Adversarial Games: A Novel Deep Reinforcement Learning Framework","authors":"Chenglu Sun;Shuo Shen;Deyi Xue;Wenzhi Tao;Zixia Zhou","doi":"10.1109/TG.2024.3520970","DOIUrl":"https://doi.org/10.1109/TG.2024.3520970","url":null,"abstract":"Deep reinforcement learning (DRL) has emerged as a leading technique for designing AI-bots in the gaming industry. However, practical implementation of DRL-trained bots often encounter two significant challenges: improving strength and diversifying strategies to satisfy player expectations. We observe that the strength of AI-bots are intrinsically tied to the diversity of emerged strategies. Considering this relationship, we introduce diversity is strength (DIS), a novel DRL training framework capable of concurrently training multiple types of AI-bots for adversarial games. These bots are interconnected through an elaborated history model pool (HMP) structure, thereby improving their strength and strategy diversity to tackle the aforementioned challenges. We further devise a model evaluation and sampling scheme to form the HMP, identify superior models, and enrich the model strategies. The DIS can generate diverse and reliable strategies without the need for human data. This method is validated by achieving first-place finishes in two AI competitions based on complex adversarial games, including Google Research Football and Olympic Games. Experiments demonstrate that bots trained using DIS attain an excellent performance and plentiful strategies. Specifically, diversity analysis demonstrates that the trained bots possess a wealth of strategies, and ablation studies confirm the beneficial impact of the designed modules on the training process.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"522-535"},"PeriodicalIF":1.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Guest Editorial: Special Issue on Human Centered AI in Game Evaluation 嘉宾评论：关于游戏评估中以人为本的AI的特刊

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-12-17 DOI: 10.1109/TG.2024.3507232

Alena Denisova;Diego Perez-Liebana;Vanessa Volz;Julian Frommel;Sahar Asadi

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IEEE Transactions on Games Publication Information IEEE游戏出版信息汇刊

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-12-17 DOI: 10.1109/TG.2024.3513515

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IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-12-17 DOI: 10.1109/TG.2024.3513513

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Real-Time Player Tracking Framework on MOBA Game Video Through Object Detection 基于目标检测的MOBA游戏视频实时玩家跟踪框架

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-12-11 DOI: 10.1109/TG.2024.3515140

Dae-Wook Kim;Sung-Yun Park;Seong-Il Yang;Sang-Kwang Lee

{"title":"Real-Time Player Tracking Framework on MOBA Game Video Through Object Detection","authors":"Dae-Wook Kim;Sung-Yun Park;Seong-Il Yang;Sang-Kwang Lee","doi":"10.1109/TG.2024.3515140","DOIUrl":"https://doi.org/10.1109/TG.2024.3515140","url":null,"abstract":"The multiplayer online battle arena (MOBA) genre boasts the largest audience in esports, leading to extensive research in esports analysis targeting MOBA games. However, due to the limited availability of openly accessible data or application programming interface (API), most research has been focused on <italic>Dota 2</i> and cannot be easily extended to other MOBA games. In this article, we present a novel framework that revolutionizes real-time player trajectory extraction directly from the game screen of <italic>League of Legends</i> (<italic>LoL</i>) through object detection. To mitigate reliance on APIs, the proposed framework includes a process that generates synthetic images as training data for object detection, detects the positions of the game characters from the minimap, and considers temporal relationships to ensure stable trajectory acquisition against occlusion. For evaluation purposes, we generate ground truth data from <italic>LoL</i> replays and introduce the concept of occlusion tolerance. Our proposed framework undergoes evaluation and analysis in terms of trajectory extraction accuracy with occlusion tolerance, the significance of synthetic image elements, class-by-class detection accuracy, and processing time. Our framework opens new avenues for esports analysis. We envision its potential extension to other games lacking APIs, provided that they feature a minimap displaying game characters.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"498-509"},"PeriodicalIF":1.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0