IEEE Transactions on Games最新文献_第7页

Neural Network-Based Information Set Weighting for Playing Reconnaissance Blind Chess 基于神经网络的信息集加权用于下侦察盲棋

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-10 DOI: 10.1109/TG.2024.3425803

Timo Bertram;Johannes Fürnkranz;Martin Müller

{"title":"Neural Network-Based Information Set Weighting for Playing Reconnaissance Blind Chess","authors":"Timo Bertram;Johannes Fürnkranz;Martin Müller","doi":"10.1109/TG.2024.3425803","DOIUrl":"10.1109/TG.2024.3425803","url":null,"abstract":"In imperfect information games, the game state is generally not fully observable to players. Therefore, good gameplay requires policies that deal with the different information that is hidden from each player. To combat this, effective algorithms often reason about information sets; the sets of all possible game states that are consistent with a player's observations. While there is no way to distinguish between the states within an information set, this property does not imply that all states are equally likely to occur in play. We extend previous research on assigning weights to the states in an information set in order to facilitate better gameplay in the imperfect information game of reconnaissance blind chess (RBC). For this, we train two different neural networks, which estimate the likelihood of each state in an information set from historical game data. Experimentally, we find that a Siamese neural network is able to achieve higher accuracy and is more efficient than a classical convolutional neural network for the given domain. Finally, we evaluate an RBC-playing agent that is based on the generated weightings and compare different parameter settings that influence how strongly it should rely on them. The resulting best player is ranked 5th on the public leaderboard.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"960-970"},"PeriodicalIF":1.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10592629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141588601","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

Codeless3D: Design and Usability Evaluation of a Low-Code Tool for 3-D Game Generation Codeless3D：三维游戏生成低代码工具的设计与可用性评估

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-09 DOI: 10.1109/TG.2024.3424894

Christina Volioti;Vasileios Martsis;Apostolos Ampatzoglou;Euclid Keramopoulos;Alexander Chatzigeorgiou

{"title":"Codeless3D: Design and Usability Evaluation of a Low-Code Tool for 3-D Game Generation","authors":"Christina Volioti;Vasileios Martsis;Apostolos Ampatzoglou;Euclid Keramopoulos;Alexander Chatzigeorgiou","doi":"10.1109/TG.2024.3424894","DOIUrl":"10.1109/TG.2024.3424894","url":null,"abstract":"In recent years, the game industry has experienced significant growth from both a financial and a social viewpoint. Developing compelling games that rely on novel content is a challenge for 3-D firms, especially in terms of meeting the diverse expectations of end users. Game development is performed by multidisciplinary teams of professionals, in which game/level designers play a crucial role. Inevitably, they often depend on programmers for technical implementations creating bottlenecks, even for prototyping purposes. This issue has raised the need for introducing efficient low-code environments that empower individuals without programming expertise to develop 3-D games. This work introduces Codeless3D, a prototype for low-code 3-D game creation by nonprogrammers. The proposed approach and the tool aim to reduce design and development time, bridging the gap between conceptualization and production. To evaluate the usefulness of Codeless3D, in terms of usability and its vision, an evaluation study was conducted. The findings suggested that Codeless3D effectively reduces design and development time for stakeholders in the game development field. Overall, this article contributes to the emerging trend of low-code tools in the entertainment domain and offers insights for further improvements in game design and development processes.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"296-307"},"PeriodicalIF":1.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574661","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

Bidding Efficiently in Simultaneous Ascending Auctions With Budget and Eligibility Constraints Using Simultaneous Move Monte Carlo Tree Search 利用同步移动蒙特卡洛树搜索在有预算和资格限制的同步升序拍卖中高效竞价

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-09 DOI: 10.1109/TG.2024.3424246

Alexandre Pacaud;Aurelien Bechler;Marceau Coupechoux

{"title":"Bidding Efficiently in Simultaneous Ascending Auctions With Budget and Eligibility Constraints Using Simultaneous Move Monte Carlo Tree Search","authors":"Alexandre Pacaud;Aurelien Bechler;Marceau Coupechoux","doi":"10.1109/TG.2024.3424246","DOIUrl":"10.1109/TG.2024.3424246","url":null,"abstract":"For decades, simultaneous ascending auction (SAA) has been the most popular mechanism used for spectrum auctions. It has recently been employed by many countries for the allocation of 5G licences. Although SAA presents relatively simple rules, it induces a complex strategic game for which the optimal bidding strategy is unknown. Considering the fact that sometimes billions of euros are at stake in an SAA, establishing an efficient bidding strategy is crucial. In this work, we model the auction as a <inline-formula><tex-math>$n$</tex-math></inline-formula>-player simultaneous move game with complete information and propose the first efficient bidding algorithm that tackles simultaneously its four major strategic issues: the <italic>exposure problem, the <italic>own price effect, <italic>budget constraints, and the <italic>eligibility management problem. Our solution, called <inline-formula><tex-math>$text{SMS}^alpha$</tex-math></inline-formula>, is based on simultaneous move Monte Carlo Tree Search and relies on a new method for the prediction of closing prices. By introducing a new reward function in <inline-formula><tex-math>$SMS^alpha$</tex-math></inline-formula>, we give the possibility to bidders to define their own level of risk-aversion. Through extensive numerical experiments on instances of realistic size, we show that <inline-formula><tex-math>$text{SMS}^alpha$</tex-math></inline-formula> largely outperforms state-of-the-art algorithms, notably by achieving higher expected utility while taking less risks.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 1","pages":"210-223"},"PeriodicalIF":1.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574665","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

Investigating the Effect of Emotional Matching Between Game and Background Music on Game Experience in a Valence–Arousal Space 探究游戏与背景音乐之间的情感匹配对价值-情绪空间中游戏体验的影响

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-08 DOI: 10.1109/TG.2024.3424459

JaeYoung Moon;EunHye Cho;Yeabon Jo;KyungJoong Kim;Eunsung Song

引用次数: 0

Exploring Gameplay and Learning in a Narrative-Centered Digital Game for Elementary Science Education 探索以叙事为中心的小学科学教育数字游戏中的游戏性和学习性

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-08 DOI: 10.1109/TG.2024.3424689

Seung Lee;Bradford Mott;Jessica Vandenberg;Hiller A. Spires;James Lester

{"title":"Exploring Gameplay and Learning in a Narrative-Centered Digital Game for Elementary Science Education","authors":"Seung Lee;Bradford Mott;Jessica Vandenberg;Hiller A. Spires;James Lester","doi":"10.1109/TG.2024.3424689","DOIUrl":"10.1109/TG.2024.3424689","url":null,"abstract":"Recent years have seen increased exploration of the transformative potential of digital games for K-12 education. Narrative-centered digital games for learning integrate complex problem solving within compelling interactive stories. By leveraging the inherent structure of narrative and the engaging interactions afforded by commercial game engines, narrative-centered digital games for learning engage students in situated learning activities. This article presents details on the iterative design and development of a narrative-centered digital game for learning that focuses on science education for fifth-grade students. We then explore how student gameplay and learning relate by leveraging interaction log data from over 700 students playing the game. Specifically, we analyze student gameplay achievements using clustering and examine how gameplay and learning outcomes differ among the groups identified. Furthermore, we investigate if gender has an effect on student learning within the groups and what gender differences are found within the groups. The findings show that students who complete more quests and earn better in-game rewards achieve higher learning gains, and while differences exist in game playing characteristics between males and females the learning outcomes are similar.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"947-959"},"PeriodicalIF":1.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574662","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

Markov Decision Process-Based Artificial Intelligence With Card-Playing Strategy and Free-Playing Right Exploration for Four-Player Card Game Big2 基于马尔可夫决策过程的人工智能与四人纸牌游戏 Big2 的出牌策略和自由出牌权探索

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-08 DOI: 10.1109/TG.2024.3424431

Lien-Wu Chen;Yiou-Rwong Lu

{"title":"Markov Decision Process-Based Artificial Intelligence With Card-Playing Strategy and Free-Playing Right Exploration for Four-Player Card Game Big2","authors":"Lien-Wu Chen;Yiou-Rwong Lu","doi":"10.1109/TG.2024.3424431","DOIUrl":"10.1109/TG.2024.3424431","url":null,"abstract":"The popular East Asian card game <italic>Big2 involves rules that do not allow players to view each other's hand cards, making artificial intelligence face challenges in performing well in this game. Based on Markov decision processes (MDPs) that can handle partially observable and stochastic information, we design the Big2MDP framework to explore card-playing strategies that minimize losing risks while maximizing scoring opportunities for the <italic>Big2 game. According to our review of relevant research, this is the first <italic>Big2 artificial intelligence framework with the following features: first, the ability to simultaneously consider scoring and losing points to make the best winning decisions with minimal losing risk, second, the capability to predict multiple opponents' actions to optimize the decision-making, and third, the adaptability to compete for the free-playing right to change card combinations at the essential moment. We implement a system of four-player card game <italic>Big2 on the Android platform to validate the feasibility and effectiveness of Big2MDP. Experimental results show that Big2MDP outperforms existing artificial intelligence methods, achieving the highest win rate and the least number of losing points as competing against both computer and human players in <italic>Big2 games.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"267-281"},"PeriodicalIF":1.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574663","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

DanZero+: Dominating the GuanDan Game Through Reinforcement Learning 丹零+：通过强化学习统治关丹游戏

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-03 DOI: 10.1109/TG.2024.3422396

Youpeng Zhao;Yudong Lu;Jian Zhao;Wengang Zhou;Houqiang Li

{"title":"DanZero+: Dominating the GuanDan Game Through Reinforcement Learning","authors":"Youpeng Zhao;Yudong Lu;Jian Zhao;Wengang Zhou;Houqiang Li","doi":"10.1109/TG.2024.3422396","DOIUrl":"10.1109/TG.2024.3422396","url":null,"abstract":"Recent advancements have propelled artificial intelligence (AI) to showcase expertise in intricate card games, such as \u0000<italic>Mahjong\u0000, \u0000<italic>DouDizhu\u0000, and \u0000<italic>Texas Hold'em\u0000. In this work, we aim to develop an AI program for an exceptionally complex and popular card game called \u0000<italic>GuanDan\u0000. This game involves four players engaging in both competitive and cooperative play throughout a long process, posing great challenges for AI due to its expansive state and action space, long episode length, and complex rules. Employing reinforcement learning techniques, specifically deep Monte Carlo, and a distributed training framework, we first put forward an AI program named DanZero. Evaluation against baseline AI programs based on heuristic rules highlights the outstanding performance of our bot. Besides, in order to further enhance the AI's capabilities, we apply proximal policy optimization to \u0000<italic>GuanDan\u0000 on the basis of Danzero. To address the challenges arising from the huge action space, which will significantly impact the performance of policy-based algorithms, we adopt the pretrained model to compress the action space and integrate action features into the model to bolster its generalization capabilities. Using these techniques, we manage to obtain a new \u0000<italic>GuanDan\u0000 AI program DanZero+, which achieves a superior performance compared to DanZero.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"914-926"},"PeriodicalIF":1.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547879","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

Zero-Shot Reasoning: Personalized Content Generation Without the Cold Start Problem 零点推理：无冷启动问题的个性化内容生成

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-07-02 DOI: 10.1109/TG.2024.3421590

Davor Hafnar;Jure Demšar

{"title":"Zero-Shot Reasoning: Personalized Content Generation Without the Cold Start Problem","authors":"Davor Hafnar;Jure Demšar","doi":"10.1109/TG.2024.3421590","DOIUrl":"10.1109/TG.2024.3421590","url":null,"abstract":"Procedural content generation uses algorithmic techniques to create large amounts of new content for games at much lower production costs. To improve its quality, in newer approaches, procedural content generation utilizes machine learning. However, these methods usually require expensive collection of large amounts of data, as well as the development and training of fairly complex learning models, which can be both extremely time-consuming and expensive. The core of our research is to explore whether we can lower the barrier to the use of personalized procedural content generation through a more practical and generalizable approach with large language models. Matching game content to player preferences benefits both players, by enhancing enjoyment, and developers, who rely on player satisfaction for monetization. Therefore, this article introduces a new method for personalization by using large language models to suggest levels based on ongoing gameplay data from each player. We compared the levels generated using our approach with levels generated with more traditional procedural generation techniques. Our easily reproducible method has proven viable in a production setting and outperformed levels generated by traditional methods in two aspects—the player's rating of levels and the probability that a player will not quit the game mid-level.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"17 2","pages":"257-266"},"PeriodicalIF":1.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141526435","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

Using Reinforcement Learning to Generate Levels of Super Mario Bros. With Quality and Diversity 使用强化学习生成高质量和多样化的《超级马里奥兄弟》关卡

IF 1.7 4区计算机科学

IEEE Transactions on Games Pub Date : 2024-06-19 DOI: 10.1109/TG.2024.3416472

SangGyu Nam;Chu-Hsuan Hsueh;Pavinee Rerkjirattikal;Kokolo Ikeda

{"title":"Using Reinforcement Learning to Generate Levels of Super Mario Bros. With Quality and Diversity","authors":"SangGyu Nam;Chu-Hsuan Hsueh;Pavinee Rerkjirattikal;Kokolo Ikeda","doi":"10.1109/TG.2024.3416472","DOIUrl":"10.1109/TG.2024.3416472","url":null,"abstract":"Procedural content generation (PCG) is essential in game development, automating content creation to meet various criteria such as playability, diversity, and quality. This article leverages reinforcement learning (RL) for PCG to generate \u0000<italic>Super Mario Bros.\u0000 levels. We formulate the problem into a Markov decision process (MDP), with rewards defined using player enjoyment-based evaluation functions. Challenges in level representation and difficulty assessment are addressed by conditional generative adversarial networks and human-like artificial intelligence agents that mimic aspects of human input inaccuracies. This ensures that the generated levels are appropriately challenging from human perspectives. Furthermore, we enhance content quality through virtual simulation, which assigns rewards to intermediate actions to address a credit assignment problem. We also ensure diversity through a diversity-aware greedy policy, which chooses not-bad-but-distant actions based on \u0000<inline-formula><tex-math>$Q$</tex-math></inline-formula>\u0000-values. These processes ensure the production of diverse and high-quality \u0000<italic>Super Mario\u0000 levels. Human subject evaluations revealed that levels generated from our approach exhibit natural connection, appropriate difficulty, nonmonotony, and diversity, highlighting the effectiveness of our proposed methods. The novelty of our work lies in the innovative solutions we propose to address challenges encountered in employing the PCG via RL method in \u0000<italic>Super Mario Bros.\u0000, contributing to the field of PCG for game development.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"807-820"},"PeriodicalIF":1.7,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944751","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 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 : 2024-06-17 DOI: 10.1109/TG.2024.3409129

引用次数: 0