IEEE Transactions on Games最新文献_第5页

Collaborative AI in the Geometry Friends Game Competition 几何之友游戏比赛中的协作AI

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-10-29 DOI: 10.1109/TG.2024.3487309

Alberto Almagro;Juan Carlos Llamas-Núñez;Antonio A. Sánchez-Ruiz;Belén Díaz-Agudo

引用次数: 0

Large Language Models as Narrative Planning Search Guides 作为叙事计划搜索指南的大型语言模型

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-10-29 DOI: 10.1109/TG.2024.3487416

Rachelyn Farrell;Stephen G. Ware

引用次数: 0

Image-Based Video Game Asset Generation and Evaluation Using Deep Learning: A Systematic Review of Methods and Applications 使用深度学习的基于图像的视频游戏资产生成和评估：方法和应用的系统回顾

IF 2.8 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-10-28 DOI: 10.1109/TG.2024.3487054

Rafael Ribeiro;Alexandre Valle de Carvalho;Nelson Bilber Rodrigues

{"title":"Image-Based Video Game Asset Generation and Evaluation Using Deep Learning: A Systematic Review of Methods and Applications","authors":"Rafael Ribeiro;Alexandre Valle de Carvalho;Nelson Bilber Rodrigues","doi":"10.1109/TG.2024.3487054","DOIUrl":"https://doi.org/10.1109/TG.2024.3487054","url":null,"abstract":"Creating content for digital video game is an expensive segment of the development process, and many techniques have been explored to automate it. Much of the generated content is graphical, ranging from textures and sprites to typographical elements and user interfaces. Numerous techniques have been explored to automate the generation of these assets, with recent advancements incorporating artificial intelligence methodologies, such as deep learning generative models. This study comprehensively surveys the literature from 2016 onward, focusing on using machine learning to generate image-based assets for video game development, reviewing the deep learning approaches employed, and analyzing the specific challenges found. Specifically, the deep learning approaches employed, the problems addressed within the domain, and the metrics used for evaluating the results. The study demonstrates a knowledge gap in generative methods for some types of video game assets. In addition, applicability and effectiveness of the most used evaluation metrics in the literature are studied. As future research prospects, with the increase in popularity of generative AI, the adoption of such techniques will be seen in automation processes.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 3","pages":"622-630"},"PeriodicalIF":2.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073206","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

CuDA2: An Approach for Incorporating Traitor Agents Into Cooperative Multiagent Systems 协作多智能体系统中叛逆者智能体的集成方法

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-10-24 DOI: 10.1109/TG.2024.3485726

Zhen Chen;Yong Liao;Youpeng Zhao;Zipeng Dai;Jian Zhao

{"title":"CuDA2: An Approach for Incorporating Traitor Agents Into Cooperative Multiagent Systems","authors":"Zhen Chen;Yong Liao;Youpeng Zhao;Zipeng Dai;Jian Zhao","doi":"10.1109/TG.2024.3485726","DOIUrl":"https://doi.org/10.1109/TG.2024.3485726","url":null,"abstract":"Cooperative multiagent reinforcement learning (CMARL) strategies are well known to be vulnerable to adversarial perturbations. Previous works on adversarial attacks have primarily focused on glass-box attacks that directly perturb the states or actions of victim agents, often in scenarios with a limited number of attacks. However, gaining complete access to victim agents in real-world environments is exceedingly difficult. To create more realistic adversarial attacks, we introduce a novel method that involves injecting traitor agents into the CMARL system. We model this problem as a traitor Markov decision process (TMDP), where traitors cannot directly attack the victim agents but can influence their formation or positioning through collisions. In TMDP, traitors are trained using the same MARL algorithm as the victim agents, with their reward function set as the negative of the victim agents' reward. Despite this, the training efficiency for traitors remains low because it is challenging for them to directly associate their actions with the victim agents' rewards. To address this issue, we propose the curiosity-driven adversarial attack (CuDA2) framework. CuDA2 enhances the efficiency and aggressiveness of attacks on the specified victim agents' policies while maintaining the optimal policy invariance of the traitors. Specifically, we employ a pretrained random network distillation module, where the extra reward generated by the RND module encourages traitors to explore states unencountered by the victim agents. Extensive experiments on various scenarios from SMAC demonstrate that our CuDA2 framework offers comparable or superior adversarial attack capabilities compared to other baselines.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"397-407"},"PeriodicalIF":1.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308312","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

Toward Immersive Computational Storytelling: Card-Framework for Enhanced Persona-Driven Dialogues 走向沉浸式计算叙事：增强人物角色驱动对话的卡片框架

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-09-24 DOI: 10.1109/TG.2024.3466898

Liao Bingli;Danilo Vasconcellos Vargas

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 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-09-16 DOI: 10.1109/TG.2024.3447396

引用次数: 0

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

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-09-16 DOI: 10.1109/TG.2024.3447400

引用次数: 0

Large Language Models and Games: A Survey and Roadmap 大型语言模型和游戏：调查与路线图

IF 2.3 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-09-13 DOI: 10.1109/tg.2024.3461510

Roberto Gallotta, Graham Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis

引用次数: 0

Investigating Efficiency of Free-for-All Models in a Matchmaking Context 调查匹配背景下自由选择模式的效率

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-09-11 DOI: 10.1109/TG.2024.3459613

Emil Gensby;Bryan S. Weber;Anders H. Christiansen

{"title":"Investigating Efficiency of Free-for-All Models in a Matchmaking Context","authors":"Emil Gensby;Bryan S. Weber;Anders H. Christiansen","doi":"10.1109/TG.2024.3459613","DOIUrl":"10.1109/TG.2024.3459613","url":null,"abstract":"We explore several popular (and unpopular) systems for matchmaking and ranking in free-for-all environments. The commonplace existing methods involve the reinterpretation of established two-player ranking systems (i.e., Elo/Glicko) and decomposing multiplayer games into a set of multiple one-versus-one pairings. This decomposition, while commonplace, is not part of the intended use-case of these two-player ranking systems. We are the first to formally explore this ad-hoc usage and reassuringly find evidence that it converges to correct values. Second, we identify a method that appears to dominate what appears to be the most common publicly used method. At the same time, this novel method maintains fidelity to many games for which there is no “second place,” whereas in other systems, second place winners are given a large boost in rankings. Third, some idiosyncrasies about the reward structure and distribution of each of the systems are identified, which may affect user experience and satisfaction. This system was tested by simulation and deployment in a real world matchmaking system with over 135 000 games played. Our tests suggest it converges on appropriate player rank at a similar or better rate as the most popular alternative.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"374-383"},"PeriodicalIF":1.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218412","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

A Framework for Predicting the Impact of Game Balance Changes Through Meta Discovery 通过元发现预测游戏平衡变化影响的框架

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-09-10 DOI: 10.1109/TG.2024.3457822

Akash Saravanan;Matthew Guzdial

引用次数: 0