IEEE Transactions on Games最新文献_第3页

Impact of Multiple Sensory Cues in Immersive Virtual Reality: Evidence From a Full-Body Haptic Suit 沉浸式虚拟现实中多重感官线索的影响：来自全身触觉套装的证据

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-01-27 DOI: 10.1109/TG.2025.3534571

Polona Caserman;Benjamin Lukas Schnitzer;Stefan Göbel

{"title":"Impact of Multiple Sensory Cues in Immersive Virtual Reality: Evidence From a Full-Body Haptic Suit","authors":"Polona Caserman;Benjamin Lukas Schnitzer;Stefan Göbel","doi":"10.1109/TG.2025.3534571","DOIUrl":"https://doi.org/10.1109/TG.2025.3534571","url":null,"abstract":"Previous works have shown that providing visuo-tactile stimuli synchronously or/and congruent to an artificial and physical body part cause users to perceive the artificial body part as their own. Although this illusion over fake limbs has been extended to immersive virtual reality (VR), the effect of haptic feedback on full body has not been yet examined in depth. This study investigates the effects of visuo-tactile and visuo-motor stimulation on inducing embodiment within VR using a full-body motion capture suit with haptic feedback. Our findings reveal that synchronous and congruent visuo-tactile stimulation significantly enhances the sense of embodiment compared to incongruent visuo-tactile or visuo-motor stimulation alone. Further findings related to heart activity and subjective ratings suggest that incongruent visuo-tactile stimulation or the complete absence of haptic feedback may increase the stress level after the threat event, indicating body disconnection or body disownership. These findings have practical implications for VR system design, particularly in gaming and interactive entertainment. Our results showing the effects of visuo-tactile in addition to the visuo-motor stimuli provide valuable insights for optimizing the sense of embodiment in VR experiences. Ultimately, our research underlines the potential of haptic technologies in creating more engaging and immersive virtual environments.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 3","pages":"710-719"},"PeriodicalIF":2.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073441","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

Procedural Content Generation for Cooperative Games—A Systematic Review 合作游戏的程序内容生成

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-01-15 DOI: 10.1109/TG.2025.3530419

José Bernardo Rocha;Rui Prada

{"title":"Procedural Content Generation for Cooperative Games—A Systematic Review","authors":"José Bernardo Rocha;Rui Prada","doi":"10.1109/TG.2025.3530419","DOIUrl":"https://doi.org/10.1109/TG.2025.3530419","url":null,"abstract":"In this article, we conduct a systematic review on procedural content generation (PCG) for cooperative games. Our main goal was exploring the state of the art in this area of research, and, in particular, its application to level generation for cooperative games. Another objective was determining how generating content for cooperative games could be different from generating content for other types of games. Our results showed a lack of research in this particular application of PCG. Despite this, we found a variety of use cases of PCG for cooperative games, most of them related to <italic>autonomous generation and <italic>co-creative and mixed-initiative design. In addition, we found a variety of methods and techniques being used to generate levels for cooperative games that covered the broad categories we used to condense this information. Concerning the differences between generating content for cooperative games and generating content for other types of games, the studies we found suggested that PCG for cooperative games should take into account the design specificities of cooperative games—the types of tasks, cooperative game design patterns and game mechanics used to incentivize and support cooperative gameplay.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 3","pages":"545-557"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073222","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

Developing Agents for Complete DouDizhu Game Enhanced With Concurrent and Multistage Training Methods 用并行和多阶段训练方法增强的斗地主游戏完整代理的开发

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-01-15 DOI: 10.1109/TG.2025.3530476

Chuanfa Li;Kiminori Matsuzaki

{"title":"Developing Agents for Complete DouDizhu Game Enhanced With Concurrent and Multistage Training Methods","authors":"Chuanfa Li;Kiminori Matsuzaki","doi":"10.1109/TG.2025.3530476","DOIUrl":"https://doi.org/10.1109/TG.2025.3530476","url":null,"abstract":"<italic>DouDizhu is an imperfect information game involving three players, with two different bidding and cardplay phases. The large state and action spaces of the game add to its complexity. Previous studies of <italic>DouDizhu have primarily concentrated on the cardplay phase, which is the more challenging phase. As research on the cardplay agent deepens, researchers have become interested in how to train agents for the complete <italic>DouDizhu game. However, recent studies have overlooked that a poor bidding agent, which always bids 3, hinders the training of the cardplay agent. To enhance the performance of the cardplay agent (and accordingly the complete agent), this study employs a concurrent training method, in which a bidding agent is trained together with a cardplay agent and can quickly learn a good bidding play. To overcome the training difficulty encountered when the complete agent trained with the game score target, we propose a multistage training method: in the initial stage, the complete agent aims to maximize the win rate; in subsequent stages, it gradually shifts to targeting the maximum game score. Our <monospace>CT-MS3-FullDouZero+</monospace> agent achieves the highest average game score at <bold>0.228 <inline-formula><tex-math>$pm$</tex-math></inline-formula> <bold>0.060, while two competing agents, the state-of-the-art <monospace>CoG23+PerfectDou</monospace> and <monospace>ST-FullDouZero+</monospace>, recorded only <bold><inline-formula><tex-math>$-$</tex-math></inline-formula>0.002 <inline-formula><tex-math>$pm$</tex-math></inline-formula> <bold>0.058 and <bold><inline-formula><tex-math>$-$</tex-math></inline-formula>0.226 <inline-formula><tex-math>$pm$</tex-math></inline-formula> <bold>0.056, respectively.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 3","pages":"676-685"},"PeriodicalIF":2.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073317","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

BenchING: A Benchmark for Evaluating Large Language Models in Following Structured Output Format Instruction in Text-Based Narrative Game Tasks benchching：在基于文本的叙事游戏任务中评估遵循结构化输出格式指令的大型语言模型的基准

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-01-14 DOI: 10.1109/TG.2025.3529117

Pittawat Taveekitworachai;Mury F. Dewantoro;Yi Xia;Pratch Suntichaikul;Ruck Thawonmas

{"title":"BenchING: A Benchmark for Evaluating Large Language Models in Following Structured Output Format Instruction in Text-Based Narrative Game Tasks","authors":"Pittawat Taveekitworachai;Mury F. Dewantoro;Yi Xia;Pratch Suntichaikul;Ruck Thawonmas","doi":"10.1109/TG.2025.3529117","DOIUrl":"https://doi.org/10.1109/TG.2025.3529117","url":null,"abstract":"In this article, we present BenchING, a new benchmark for evaluating large language models (LLMs) on their ability to follow structured output format instructions in text-based procedural content generation (PCG) tasks. The ability to condition LLMs to output in specified formats proves useful, as downstream components in LLM-integrated games often require structured outputs for exchanging information. However, there is a gap in evaluating this aspect of LLMs, especially in narrative PCG tasks, making it difficult to select LLMs and design games or applications integrating these LLMs. To demonstrate the potential of our benchmark, we evaluate nine LLMs for their ability to generate parseable formatted outputs using five selected text-based PCG tasks. We report on the performance of these LLMs on these tasks. In addition, we categorize more detailed error types and propose solutions by utilizing LLMs to fix these errors. We also conduct a scaling study, investigating an emergent point of LLMs for their ability to fix malformed formatted content using eight quantized LLMs with varying original sizes from 0.62 to 72.3 B. Furthermore, we perform a qualitative study to assess the quality of the generated content. We make our source code and raw data available for future research.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 3","pages":"665-675"},"PeriodicalIF":2.8,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073401","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

WemiEnv: An Open-Source Reinforcement Learning Platform for WeChat Mini-Games 微环境：b微信小游戏的开源强化学习平台

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-01-13 DOI: 10.1109/TG.2025.3528371

Longxiang Shi;Qianchen Ding;Jingzhe Hou;Binbin Zhou;Canghong Jin;Ye Tao;Jinling Wei;Shijian Li

{"title":"WemiEnv: An Open-Source Reinforcement Learning Platform for WeChat Mini-Games","authors":"Longxiang Shi;Qianchen Ding;Jingzhe Hou;Binbin Zhou;Canghong Jin;Ye Tao;Jinling Wei;Shijian Li","doi":"10.1109/TG.2025.3528371","DOIUrl":"https://doi.org/10.1109/TG.2025.3528371","url":null,"abstract":"The popularity of mobile games has surged in recent years. Along with mobile games, the emergence of mini-games has recently raised attention. Compared to traditional mobile games, mini-games are more lightweight and platform-independent with low development cost, which has attracted thousands of developers and users. WeChat mini-games platform is one of the most popular platforms with over 100 000 mini-games. The diversity and variety of WeChat mini-games make it an ideal platform for training reinforcement learning (RL) agents. In contrast, most of the existing RL benchmark environments are equipped with predetermined games, which are always limited to several genres and lack the utilization of new and diverse games. To utilize the WeChat mini-games for RL research, in this article, we propose WemiEnv, a lightweight, easy-to-use and open-source platform for RL research towards WeChat mini-games. WemiEnv is built on the WeChat developer tools and allows RL agents to interact with the mini-games. WemiEnv also supports user-customized mini-games, requiring users to implement only a few interface functions within WemiEnv API. We also provide six popular mini-games: <italic>Space Fighter, <italic>Flip, 2048, <italic>Flappy Bird, <italic>Timberman, and <italic>Snake as ready-to-use tasks. Experiments were conducted with the OpenAI Spinning Up library for RL baselines on the provided tasks to test the usability of WemiEnv.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 3","pages":"642-651"},"PeriodicalIF":2.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073236","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

CECILIA: A Toolkit for Visual Game Content Exploration and Modification 一个视觉游戏内容探索和修改的工具包

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2025-01-13 DOI: 10.1109/TG.2025.3528513

Philipp Fleck;Michael Hochörtler;David Kastl;Georg Gotschier;Johanna Pirker;Dieter Schmalstieg

引用次数: 0

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}

引用次数: 0

Identifying Strategies in Dominion Using Playtrace Clustering 利用Playtrace聚类识别《Dominion》中的策略

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-12-23 DOI: 10.1109/TG.2024.3520862

Anthony Owen

引用次数: 0

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}

引用次数: 0

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

引用次数: 0