IEEE Transactions on Games最新文献_第6页

Call for Auxiliary Papers IEEE Conference on Games 2024 征集辅助论文 IEEE 2024 年游戏大会

IF 2.3 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-18 DOI: 10.1109/TG.2024.3371853

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 : 2024-03-18 DOI: 10.1109/TG.2024.3369433

引用次数: 0

Nested Wave Function Collapse Enables Large-Scale Content Generation 嵌套波函数折叠实现大规模内容生成

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-18 DOI: 10.1109/TG.2024.3377637

Yuhe Nie;Shaoming Zheng;Zhan Zhuang;Julian Togelius

引用次数: 0

AstroBug: Automatic Game Bug Detection Using Deep Learning AstroBug：利用深度学习自动检测游戏错误

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-17 DOI: 10.1109/TG.2024.3402626

Elham Azizi;Loutfouz Zaman

引用次数: 0

GAILPG: Multiagent Policy Gradient With Generative Adversarial Imitation Learning GAILPG：多代理策略梯度与生成式对抗模仿学习

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-14 DOI: 10.1109/TG.2024.3375515

Wei Li;Shiyi Huang;Ziming Qiu;Aiguo Song

{"title":"GAILPG: Multiagent Policy Gradient With Generative Adversarial Imitation Learning","authors":"Wei Li;Shiyi Huang;Ziming Qiu;Aiguo Song","doi":"10.1109/TG.2024.3375515","DOIUrl":"10.1109/TG.2024.3375515","url":null,"abstract":"In reinforcement learning, the agents need to sufficiently explore the environment and efficiently exploit the existing experiences before finding the solution to the tasks, particularly in cooperative multiagent scenarios where the state and action spaces grow exponentially with the number of agents. Hence, enhancing the exploration ability of agents and improving the utilization efficiency of experiences are two critical issues in cooperative multiagent reinforcement learning. We propose a novel method called generative adversarial imitation learning policy gradients (GAILPG). The contributions of GAILPG are as follows: first, we integrate generative adversarial self-imitation learning into the multiagent actor–critic framework to improve the utilization efficiency of experiences, thus further assisting the policy training; second, we design a new curiosity module to enhance the exploration ability of the agents. Experimental results on the <italic>StarCraft II micromanagement benchmark demonstrate that GAILPG surpasses state-of-the-art policy-based methods and is even on par with the value-based methods and the ablation experiments validate the reasonability of the discriminator module and the curiosity module encapsulated in our method.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 1","pages":"62-75"},"PeriodicalIF":1.7,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140146697","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

The Iceberg Profile Does Not Influence the Performance of Elite League of Legends Players, but Changes With the Events of the Game 冰山轮廓不会影响《英雄联盟》精英玩家的表现，但会随着游戏事件的发生而改变

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-14 DOI: 10.1109/TG.2024.3377604

Adrián Mateo-Orcajada;Lucía Abenza-Cano;Juan Pablo Rey-López;Raquel Vaquero-Cristóbal

{"title":"The Iceberg Profile Does Not Influence the Performance of Elite League of Legends Players, but Changes With the Events of the Game","authors":"Adrián Mateo-Orcajada;Lucía Abenza-Cano;Juan Pablo Rey-López;Raquel Vaquero-Cristóbal","doi":"10.1109/TG.2024.3377604","DOIUrl":"10.1109/TG.2024.3377604","url":null,"abstract":"Little is known about the interactions between the iceberg profile, which is characterized by high vigor scores, as opposed to low scores in tension, depression, anger, fatigue, and confusion, and performance in <italic>League of Legends (LOL). For these reasons, the objectives of the present research were to analyze whether the performance was influenced by the presence of the iceberg profile before the start of the game and to determine the changes produced in the iceberg profile of esports players as a function of the final outcome of the game, the players' performance during the game, and pregame anxiety and self-confidence. The participants were players in a professional LOL esports team during a SuperLiga Orange spring split. The profile of mood states and competitive state anxiety inventory-2 questionnaires were used. Performance was assessed using in-game variables, such as game result, favorable and unfavorable plays, and kills/deaths/assists ratio. The results showed that no changes were found in the performance of the players according to the pregame iceberg profile. Changes were found in the pre- and postgame iceberg profile, according to the final outcome of the game, and the favorable and unfavorable plays. Furthermore, the psychological variables cognitive and somatic anxiety, and self-confidence, had a relationship with the presence or absence of the iceberg profile. To conclude, the iceberg profile does not seem to influence the performance of esports players, although it is modified by events that occur during the game.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 1","pages":"76-87"},"PeriodicalIF":1.7,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140146973","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

The First ChatGPT4PCG Competition 第一届 ChatGPT4PCG 竞赛

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-12 DOI: 10.1109/TG.2024.3376429

Febri Abdullah;Pittawat Taveekitworachai;Mury F. Dewantoro;Ruck Thawonmas;Julian Togelius;Jochen Renz

{"title":"The First ChatGPT4PCG Competition","authors":"Febri Abdullah;Pittawat Taveekitworachai;Mury F. Dewantoro;Ruck Thawonmas;Julian Togelius;Jochen Renz","doi":"10.1109/TG.2024.3376429","DOIUrl":"10.1109/TG.2024.3376429","url":null,"abstract":"This article summarizes the first ChatGPT4PCG competition held at the 2023 IEEE Conference on Games. The goal of the competition is to explore emergent abilities of publicly available large language models (LLMs) in performing complex tasks related to procedural content generation, specifically physics-based level generation for \u0000<italic>Angry Birds\u0000-like games. Participants are tasked with submitting their prompts for ChatGPT to generate \u0000<italic>Angry Birds\u0000-like game structures that resemble English uppercase characters. A structure is a collection of stacked game objects comprising a part of an entire \u0000<italic>Angry Birds\u0000-like level. A prompt is an input for LLMs, including ChatGPT. Two evaluation metrics, i.e., stability and similarity, are used to evaluate the submitted prompts. Stability measures the sturdiness of a structure to withstand in-game gravity, while similarity measures a structure's resemblance to the target character. With such evaluation, participants are challenged to produce not only character-like but also stable structures by utilizing prompt engineering techniques. Finally, the competition's results are discussed to provide valuable insights for future studies and competitions.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"971-980"},"PeriodicalIF":1.7,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115320","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 Hetero-Relation Transformer Network for Multiagent Reinforcement Learning 用于多代理强化学习的异关系变压器网络

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-10 DOI: 10.1109/TG.2024.3399167

Junho Park;Sukmin Yoon;Yong-Duk Kim

{"title":"A Hetero-Relation Transformer Network for Multiagent Reinforcement Learning","authors":"Junho Park;Sukmin Yoon;Yong-Duk Kim","doi":"10.1109/TG.2024.3399167","DOIUrl":"10.1109/TG.2024.3399167","url":null,"abstract":"Recently, considerable research has been focused on multiagent reinforcement learning to effectively account for each agent's relations. However, most research has focused on homogeneous multiagent systems with the same type of agents, which has limited application to heterogeneous multiagent systems. The demand for heterogeneous systems has considerably increased not only in games but also in the real world. Therefore, a technique that can properly consider relations in heterogeneous systems is required. In this article, we propose a novel transformer network called <italic>HRformer, which is based on heterogeneous graph networks that can reflect the heterogeneity and relations among agents. To this end, we design an effective linear encoding method for the transformer to receive input of the various and unique characteristics of the agents and introduce a novel encoding method to model the relations among them. Experiments are conducted in the <italic>StarCraft multiagent challenge environment, the most famous heterogeneous multiagent simulation, to demonstrate the superior performance of the proposed method compared with the other existing methods in various heterogeneous scenarios. The proposed method in our simulation shows a high win rate and fast convergence speed, proving the superiority of the proposed method considering the heterogeneity of the multiagent system.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 1","pages":"138-147"},"PeriodicalIF":1.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140930216","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

Simulation-Driven Balancing of Competitive Game Levels With Reinforcement Learning 利用强化学习实现竞技游戏关卡的模拟驱动平衡

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-10 DOI: 10.1109/TG.2024.3399536

Florian Rupp;Manuel Eberhardinger;Kai Eckert

{"title":"Simulation-Driven Balancing of Competitive Game Levels With Reinforcement Learning","authors":"Florian Rupp;Manuel Eberhardinger;Kai Eckert","doi":"10.1109/TG.2024.3399536","DOIUrl":"10.1109/TG.2024.3399536","url":null,"abstract":"The balancing process for game levels in competitive two-player contexts involves a lot of manual work and testing, particularly for nonsymmetrical game levels. In this work, we frame game balancing as a procedural content generation task and propose an architecture for automatically balancing of tile-based levels within the procedural content generation via reinforcement learning framework (PCGRL) framework. Our architecture is divided into three parts: first, a level generator, second, a balancing agent, and third, a reward modeling simulation. Through repeated simulations, the balancing agent receives rewards for adjusting the level toward a given balancing objective, such as equal win rates for all players. To this end, we propose new swap-based representations to improve the robustness of playability, thereby enabling agents to balance game levels more effectively and quickly compared to traditional PCGRL. By analyzing the agent's swapping behavior, we can infer which tile types have the most impact on the balance. We validate our approach in the neural massively multiplayer online environment in a competitive two-player scenario. In this article, we present improved results, explore the applicability of the method to various forms of balancing beyond equal balancing, compare the performance to another search-based approach, and discuss the application of existing fairness metrics to game balancing.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"903-913"},"PeriodicalIF":1.7,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140929989","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

Towards Real-time G-buffer-Guided Style Transfer in Computer Games 在电脑游戏中实现实时 G-buffer 引导的风格转移

IF 2.3 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-03-04 DOI: 10.1109/tg.2024.3372829

Eleftherios Ioannou, Steve Maddock

引用次数: 0