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2019 IEEE Conference on Games (CoG)最新文献

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Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games 结合序列和聚合数据预测休闲免费游戏的流失
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848106
J. Kristensen, Paolo Burelli
{"title":"Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games","authors":"J. Kristensen, Paolo Burelli","doi":"10.1109/CIG.2019.8848106","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848106","url":null,"abstract":"In freemium games, the revenue from a player comes from the in-app purchases made and the advertisement to which that player is exposed. The longer a player is playing the game, the higher will be the chances that he or she will generate a revenue within the game. Within this scenario, it is extremely important to be able to detect promptly when a player is about to quit playing (churn) in order to react and attempt to retain the player within the game, thus prolonging his or her game lifetime. In this article we investigate how to improve the current state-of-the-art in churn prediction by combining sequential and aggregate data using different neural network architectures. The results of the comparative analysis show that the combination of the two data types grants an improvement in the prediction accuracy over predictors based on either purely sequential or purely aggregated data.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121161024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Object-Oriented State Abstraction in Reinforcement Learning for Video Games 面向对象状态抽象在电子游戏强化学习中的应用
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848099
Yu Chen, Huizhuo Yuan, Yujun Li
{"title":"Object-Oriented State Abstraction in Reinforcement Learning for Video Games","authors":"Yu Chen, Huizhuo Yuan, Yujun Li","doi":"10.1109/CIG.2019.8848099","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848099","url":null,"abstract":"We present a novel method to obtain object-oriented state representations for video games. Inspired by the mechanism of attention to objects in human vision, we try to make the agents automatically detect the important objects during the learning process. The detection is directed by the Q value based on the abstract state representations. The process does not require human prior knowledge and provides a faster and lighter way for AI playing games. We present empirical results on the Battle City game to validate our method. In comparison with raw images input and other preprocessing methods, our approach achieves better final results and uses smaller state space.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121331829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
CoG 2019 Staff
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/cig.2019.8848042
{"title":"CoG 2019 Staff","authors":"","doi":"10.1109/cig.2019.8848042","DOIUrl":"https://doi.org/10.1109/cig.2019.8848042","url":null,"abstract":"","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116433810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Fighting Game AI Using Highlight Cues for Generation of Entertaining Gameplay 利用高光提示创造有趣游戏玩法的格斗游戏AI
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848069
Ryota Ishii, Suguru Ito, R. Thawonmas, Tomohiro Harada
{"title":"A Fighting Game AI Using Highlight Cues for Generation of Entertaining Gameplay","authors":"Ryota Ishii, Suguru Ito, R. Thawonmas, Tomohiro Harada","doi":"10.1109/CIG.2019.8848069","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848069","url":null,"abstract":"In this paper, we propose a fighting game AI that selects its actions from the perspective of highlight generation using Monte-Carlo tree search (MCTS) with three highlight cues in the evaluation function. The proposed AI is targeted for being used to generate gameplay in live streaming platforms such as Twitch and YouTube where a large number of spectators watch gameplay to entertain themselves. Our results in a user study conducted using FightingICE, a fighting game platform used in an international game AI competition since 2013, show that gameplay generated by the proposed AI is more entertaining than that by a typical MCTS AI. Detailed analyses of gameplay from all the methods assessed in the user study are also given in the paper.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129860266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Pass in Human Style: Learning Soccer Game Patterns from Spatiotemporal Data 人类风格的传递:从时空数据中学习足球比赛模式
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848112
Victor Khaustov, Georgii Mola Bogdan, M. Mozgovoy
{"title":"Pass in Human Style: Learning Soccer Game Patterns from Spatiotemporal Data","authors":"Victor Khaustov, Georgii Mola Bogdan, M. Mozgovoy","doi":"10.1109/CIG.2019.8848112","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848112","url":null,"abstract":"Passing patterns in soccer are one of the key characteristics of tactical team behavior. Thus, in the course of development of believable AI soccer teams, it is necessary to ensure that human-like passes are properly simulated. We propose learning passing behavior from real-life soccer teams and share experimental results, indicating that our approach indeed allows to obtain passing patterns similar to the ones present in human tracking data. We also show that a typical rule-based soccer AI team exhibits notable differences in passing behavior in comparison with real teams.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125598451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Deep Variational Autoencoders for NPC Behaviour Classification 用于NPC行为分类的深度变分自编码器
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848095
E. S. Soares, V. Bulitko
{"title":"Deep Variational Autoencoders for NPC Behaviour Classification","authors":"E. S. Soares, V. Bulitko","doi":"10.1109/CIG.2019.8848095","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848095","url":null,"abstract":"Procedural content generation (PCG) can create novel, player-specific content in video games, including behaviours of AI-controlled non-playable characters (NPC). Here we present our first results on comparing unsupervised and supervised machine learning for procedurally generated NPC behaviours. Using an artificial life environment as a stand-in for a video game, we run artificial evolution and generate AI agents with various behaviours. We then train deep variational autoencoders on commonly evolved behaviour and measure its efficacy in detecting behaviours unseen during training. As a reference, we use an off-the-shelf deep network trained in a supervised manner to detect behaviours both seen and unseen during its training. Preliminary results demonstrate promising performance that holds even when the training set contains a mixture of several types of behaviours without proper labels.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125067548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Portability of Serious Game Software Components 严肃游戏软件组件的可移植性
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848094
W. V. D. Vegt, W. Westera, Hub Kurvers, E. Nyamsuren
{"title":"Portability of Serious Game Software Components","authors":"W. V. D. Vegt, W. Westera, Hub Kurvers, E. Nyamsuren","doi":"10.1109/CIG.2019.8848094","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848094","url":null,"abstract":"In recent studies, a component-based software engineering framework (RCSAA) has been proposed to accommodate the reuse of game software components across diverse game engines, platforms, and programming languages. This study follows up on this by a more detailed investigation of the portability of a RCSAA-compliant game software component across three principal programming languages: C#, JavaScript (TypeScript), and Java, respectively, and their integration in game engines for these languages. One operational RCSAA-compliant component in C# is taken as the starting point for porting to the other languages. For each port, a detailed analysis of language-specific features is carried out to examine and preserve the equivalence of transcompiled code. Also, implementation patterns of required RSCAA constructs are analysed for each programming language and practical workaround solutions are proposed. This study demonstrates that the software patterns and design solutions used in the RCSAA are easily portable across programming languages based on very different programming paradigms. It thereby establishes the practicability of the RSCAA architecture and the associated integration of RCSAA-compliant game components under real-world conditions.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123481285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhancing Rolling Horizon Evolution with Policy and Value Networks 利用政策和价值网络增强滚动地平线演化
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848041
Xinyao Tong, Weiming Liu, Bin Li
{"title":"Enhancing Rolling Horizon Evolution with Policy and Value Networks","authors":"Xinyao Tong, Weiming Liu, Bin Li","doi":"10.1109/CIG.2019.8848041","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848041","url":null,"abstract":"Rolling Horizon Evolutionary Algorithm (RHEA) is an online planning method for real-time game playing; its performance is closely related to the planning horizon and the search cost allowed. In this paper, we propose to learn a prior for RHEA in an offline manner by training a value network and a policy network. The value network is used to reduce the planning horizon by providing an estimation of future rewards, and the policy network is used to initialize the population, which helps to narrow down the search scope. The proposed algorithm, named prior-based RHEA (p-RHEA), trains policy and value networks by performing planning and learning iteratively. In the planning stage, the horizon-limited search is performed to improve the policies and collect training samples with the help of the learned networks. In the learning stage, the policy network and value network are trained with the collected samples to learn better prior knowledge. Experimental results on OpenAI MuJoCo tasks show that the performance of the proposed p- RHEA is significantly improved compared to that of RHEA.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123520258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Towards Multi-modal Stress Response Modelling in Competitive League of Legends 竞技英雄联盟多模态应力响应模型研究
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848004
P. M. Blom, S. Bakkes, P. Spronck
{"title":"Towards Multi-modal Stress Response Modelling in Competitive League of Legends","authors":"P. M. Blom, S. Bakkes, P. Spronck","doi":"10.1109/CIG.2019.8848004","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848004","url":null,"abstract":"With the constant rise in popularity of competitive video gaming (also known as Esports), Esports analytics has been a field of growing scientific interest in the recent years. Studies discussing player behaviour, skill learning and team performance have been conducted through Multiplayer Online Battle Arena games such as League of Legends. In this paper, we propose a multi-modal approach towards stress response modeling in competitive LoL games. We collect wearable physiological sensor data, mouse & keyboard logs and in-game data in order to study the relationship between player stress responses and in-game behaviour. We discuss the design criteria and propose future studies using the collected dataset.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116485417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Reveal-More: Amplifying Human Effort in Quality Assurance Testing Using Automated Exploration 揭示更多:在使用自动化探索的质量保证测试中放大人类的努力
2019 IEEE Conference on Games (CoG) Pub Date : 2019-08-01 DOI: 10.1109/CIG.2019.8848091
Kenneth Chang, Batu Aytemiz, Adam M. Smith
{"title":"Reveal-More: Amplifying Human Effort in Quality Assurance Testing Using Automated Exploration","authors":"Kenneth Chang, Batu Aytemiz, Adam M. Smith","doi":"10.1109/CIG.2019.8848091","DOIUrl":"https://doi.org/10.1109/CIG.2019.8848091","url":null,"abstract":"Attempting to maximize coverage of a game via human gameplay is laborious and repetitive, introducing delays in the development process. Despite the importance of quality assurance (QA) testing, QA remains an underinvested area in the technical games research community. In this paper, we show that relatively simple automatic exploration techniques can be used to multiplicatively amplify coverage of a game starting from human tester data. Instead of attempting to displace human QA efforts, we seek to grow the impact that a human tester can make. Experiments with two games for the Super Nintendo Entertainment System highlight the qualitative and quantitative differences between isolated human and machine play compared to our hybrid approach called Reveal-More. We contribute a QA testing workflow that scales with the amount of human and machine time allocated to the effort.","PeriodicalId":177208,"journal":{"name":"2019 IEEE Conference on Games (CoG)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124667529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 25
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