IEEE Transactions on Games最新文献_第8页

Piecing Together Performance: Collaborative, Participatory Research-Through-Design for Better Diversity in Games 拼凑表演：合作式、参与式研究--通过设计改善游戏的多样性

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-01-02 DOI: 10.1109/TG.2023.3349369

Daniel L. Gardner;LouAnne Boyd;Reginald T. Gardner

{"title":"Piecing Together Performance: Collaborative, Participatory Research-Through-Design for Better Diversity in Games","authors":"Daniel L. Gardner;LouAnne Boyd;Reginald T. Gardner","doi":"10.1109/TG.2023.3349369","DOIUrl":"10.1109/TG.2023.3349369","url":null,"abstract":"Digital games are a multi-billion-dollar industry whose production and consumption extend globally. Representation in games is an increasingly important topic. As those who create and consume the medium grow ever more diverse, it is essential that player or user-experience research, usability, and any consideration of how people interface with their technology are exercised through inclusive and intersectional lenses. Previous research has identified how character configuration interfaces preface white-male defaults (Gardner and Tanenbaum 2018), (Gardner and Tanenbaum 2021), and (Mastro and Behm-Morawitz 2005). This study relies on 1-on-1 play interviews where diverse participants attempt to create “themselves” in a series of games and on group design activities to explore how participants may envision more inclusive character configuration interface design. Our interview findings describe specific points of tension in the process of creating characters in existing interfaces and the sketches participants–collaborators produced to challenge the homogeneity of current interface designs. This project amplifies the perspective of diverse participants–collaborators to provide constructive implications and a series of \u0000<italic>principles</i>\u0000 for designing more inclusive character configuration interfaces, which support more diverse stories and gameworlds by reconfiguring the constraints that shape those stories and gameworlds.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 3","pages":"683-696"},"PeriodicalIF":1.7,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953765","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

Video-Based Engagement Estimation of Game Streamers: An Interpretable Multimodal Neural Network Approach 基于视频的游戏流用户参与度评估：可解释的多模态神经网络方法

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-12-29 DOI: 10.1109/TG.2023.3348230

Sicheng Pan;Gary J. W. Xu;Kun Guo;Seop Hyeong Park;Hongliang Ding

{"title":"Video-Based Engagement Estimation of Game Streamers: An Interpretable Multimodal Neural Network Approach","authors":"Sicheng Pan;Gary J. W. Xu;Kun Guo;Seop Hyeong Park;Hongliang Ding","doi":"10.1109/TG.2023.3348230","DOIUrl":"10.1109/TG.2023.3348230","url":null,"abstract":"In this article, we propose a nonintrusive and nonrestrictive multimodal deep learning model for estimating the engagement levels of game streamers. We incorporate three modalities from the streamers' videos (facial, pixel, and audio information) to train the multimodal neural network. Additionally, we introduce a novel interpretation technique that directly calculates the contribution of each modality to the model's classification performance without the need to retrain single-modality models. Experimental results demonstrate that our model achieves an accuracy of 77.2% on the test set, with the sound modality identified as a key modality for engagement estimation. By utilizing the proposed interpretation technique, we further analyze the modality contributions of the model in handling different categories and samples from various players. This enhances the model's interpretability and reveals its limitations, as well as future directions for improvement. The proposed approach and findings have potential applications in the fields of game streaming and audience analysis, as well as in domains related to multimodal learning and affective computing.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"746-757"},"PeriodicalIF":1.7,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218211","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

Latent Combinational Game Design 潜在组合游戏设计

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-12-25 DOI: 10.1109/TG.2023.3346331

Anurag Sarkar;Seth Cooper

引用次数: 0

GCMA: An Adaptive Multiagent Reinforcement Learning Framework With Group Communication for Complex and Similar Tasks Coordination GCMA：一种具有群组交流功能的自适应多代理强化学习框架，用于协调复杂和类似任务

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-12-25 DOI: 10.1109/TG.2023.3346394

Kexing Peng;Tinghuai Ma;Xin Yu;Huan Rong;Yurong Qian;Najla Al-Nabhan

{"title":"GCMA: An Adaptive Multiagent Reinforcement Learning Framework With Group Communication for Complex and Similar Tasks Coordination","authors":"Kexing Peng;Tinghuai Ma;Xin Yu;Huan Rong;Yurong Qian;Najla Al-Nabhan","doi":"10.1109/TG.2023.3346394","DOIUrl":"10.1109/TG.2023.3346394","url":null,"abstract":"Coordinating multiple agents with diverse tasks and changing goals without interference is a challenge. Multiagent reinforcement learning (MARL) aims to develop effective communication and joint policies using group learning. Some of the previous approaches required each agent to maintain a set of networks independently, resulting in no consideration of interactions. Joint communication work causes agents receiving information unrelated to their own tasks. Currently, agents with different task divisions are often grouped by action tendency, but this can lead to poor dynamic grouping. This article presents a two-phase solution for multiple agents, addressing these issues. The first phase develops heterogeneous agent communication joint policies using a group communication MARL framework (GCMA). The framework employs a periodic grouping strategy, reducing exploration and communication redundancy by dynamically assigning agent group hidden features through hypernetwork and graph communication. The scheme efficiently utilizes resources for adapting to multiple similar tasks. In the second phase, each agent's policy network is distilled into a generalized simple network, adapting to similar tasks with varying quantities and sizes. GCMA is tested in complex environments, such as \u0000<italic>StarCraft II</i>\u0000 and unmanned aerial vehicle (UAV) take-off, showing its well-performing for large-scale, coordinated tasks. It shows GCMA's effectiveness for solid generalization in multitask tests with simulated pedestrians.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 3","pages":"670-682"},"PeriodicalIF":1.7,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218213","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

IEEE Transactions on Games Publication Information IEEE 游戏论文集》出版信息

IF 2.3 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-12-15 DOI: 10.1109/TG.2023.3340069

引用次数: 0

IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 2.3 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-12-15 DOI: 10.1109/TG.2023.3340071

引用次数: 0

Call for Papers—IEEE Transactions on Games Special Issue on Computer Vision and Games 征稿启事--《IEEE 游戏论文集》计算机视觉与游戏特刊

IF 2.3 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-12-15 DOI: 10.1109/TG.2023.3338828

引用次数: 0

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games RaidEnv：探索自动平衡 Boss Raid 游戏内容的新挑战

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-11-28 DOI: 10.1109/TG.2023.3335399

Hyeon-Chang Jeon;In-Chang Baek;Cheong-mok Bae;Taehwa Park;Wonsang You;Taegwan Ha;Hoyoun Jung;Jinha Noh;Seungwon Oh;Kyung-Joong Kim

{"title":"RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games","authors":"Hyeon-Chang Jeon;In-Chang Baek;Cheong-mok Bae;Taehwa Park;Wonsang You;Taegwan Ha;Hoyoun Jung;Jinha Noh;Seungwon Oh;Kyung-Joong Kim","doi":"10.1109/TG.2023.3335399","DOIUrl":"10.1109/TG.2023.3335399","url":null,"abstract":"The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of play-testing agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in the MMORPG games. In addition, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing (ACB), and we introduce two evaluation metrics to provide guidance for AI in ACB. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline. The open-source environment is available at a GitHub repository.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 3","pages":"645-658"},"PeriodicalIF":1.7,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10330736","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Examination of the Hidden Judging Criteria in the Generative Design in Minecraft Competition 对 "Minecraft 中的生成设计 "竞赛中隐藏的评判标准的研究

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-11-07 DOI: 10.1109/TG.2023.3329763

Jean-Baptiste Hervé;Christoph Salge;Henrik Warpefelt

引用次数: 0

MDDP: Making Decisions From Different Perspectives in Multiagent Reinforcement Learning MDDP：在多代理强化学习中从不同角度做出决策

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2023-11-01 DOI: 10.1109/TG.2023.3329376

Wei Li;Ziming Qiu;Shitong Shao;Aiguo Song

{"title":"MDDP: Making Decisions From Different Perspectives in Multiagent Reinforcement Learning","authors":"Wei Li;Ziming Qiu;Shitong Shao;Aiguo Song","doi":"10.1109/TG.2023.3329376","DOIUrl":"10.1109/TG.2023.3329376","url":null,"abstract":"Multiagent reinforcement learning (MARL) has made remarkable progress in recent years. However, in most MARL methods, agents share a policy or value network, which is easy to result in similar behaviors of agents, and thus, limits the flexibility of the method to handle complex tasks. To enhance the diversity of agent behaviors, we propose a novel method, making decisions from different perspectives (MDDP). This method enables agents to switch flexibly between different policy roles and make decisions from different perspectives, which can improve the adaptability of policy learning in complex scenarios. Specifically, in MDDP, we design a new self-attention and gated recurrent unit (GRU)-based dueling architecture network (SG-DAN) to estimate the individual \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000-values. SG-DAN contains two components: 1) the new self-attention-based role-switching network (SAR) and the capable GRU-based state value estimation network (GSE). SAR takes charge of action advantage estimation and GSE is responsible for state value estimation. Experimental results on the challenging \u0000<italic>StarCraft</i>\u0000 II micromanagement benchmark not only verify the modeling reasonability of MDDP but also demonstrate its performance superiority over the related advanced approaches.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 3","pages":"621-634"},"PeriodicalIF":1.7,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134884087","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