IEEE Transactions on Computational Intelligence and AI in Games最新文献_第10页

Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach 基于玩家登录记录的在线游戏流失预测:一种频率分析方法

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-05 DOI: 10.1109/TCIAIG.2015.2401979

Emiliano G. Castro, M. Tsuzuki

{"title":"Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach","authors":"Emiliano G. Castro, M. Tsuzuki","doi":"10.1109/TCIAIG.2015.2401979","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2401979","url":null,"abstract":"The rise of free-to-play and other service-based business models in the online gaming market brought to game publishers problems usually associated to markets like mobile telecommunications and credit cards, especially customer churn. Predictive models have long been used to address this issue in these markets, where companies have a considerable amount of demographic, economic, and behavioral data about their customers, while online game publishers often only have behavioral data. Simple time series' feature representation schemes like RFM can provide reasonable predictive models solely based on online game players' login records, but maybe without fully exploring the predictive potential of these data. We propose a frequency analysis approach for feature representation from login records for churn prediction modeling. These entries (from real data) were converted into fixed-length data arrays using four different methods, and then these were used as input for training probabilistic classifiers with the k-nearest neighbors machine learning algorithm. The classifiers were then evaluated and compared using predictive performance metrics. One of the methods, the time-frequency plane domain analysis, showed satisfactory results, being able to theoretically increase the retention campaigns profits in more than 20% over the RFM approach.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"255-265"},"PeriodicalIF":0.0,"publicationDate":"2015-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2401979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592626","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: 54

Job-Level Alpha-Beta Search 工作级别Alpha-Beta搜索

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2316314

Jr-Chang Chen, I-Chen Wu, Wen-Jie Tseng, Bo-Han Lin, Chia-Hui Chang

引用次数: 13

Design and Implementation of Chinese Dark Chess Programs 中国暗棋程序的设计与实现

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2329034

Shi-Jim Yen, Cheng-Wei Chou, Jr-Chang Chen, I-Chen Wu, Kuo-Yuan Kao

引用次数: 17

Learning Behaviors of and Interactions Among Objects Through Spatio–Temporal Reasoning 基于时空推理的对象间学习行为及相互作用

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2329770

M. Ersen, Sanem Sariel

{"title":"Learning Behaviors of and Interactions Among Objects Through Spatio–Temporal Reasoning","authors":"M. Ersen, Sanem Sariel","doi":"10.1109/TCIAIG.2014.2329770","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2329770","url":null,"abstract":"In this paper, we introduce an automated reasoning system for learning object behaviors and interactions through the observation of event sequences. We use an existing system to learn the models of objects and further extend it to model more complex behaviors. Furthermore, we propose a spatio-temporal reasoning based learning method for reasoning about interactions among objects. Experience gained through learning is to be used for achieving goals by these objects. We take The Incredible Machine game (TIM) as the main testbed to analyze our system. Tutorials of the game are used to train the system. We analyze the results of our reasoning system on four different input types: a knowledge base of relations; spatial information; temporal information; and spatio-temporal information from the environment. Our analysis reveals that if a knowledge base about relations is provided, most of the interactions can be learned. We have also demonstrated that our learning method which incorporates both spatial and temporal information gives close results to that of the knowledge-based approach. This is promising as gathering spatio-temporal information does not require prior knowledge about relations. Our second analysis of the spatio-temporal reasoning method in the Electric Box computer game domain verifies the success of our approach.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"75-87"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2329770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592173","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: 9

An Enhanced Solver for the Game of Amazons 亚马逊游戏的增强型求解器

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2309077

Jiaxing Song, Martin Müller

引用次数: 7

Sequential Halving Applied to Trees 适用于树的顺序减半

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2317737

T. Cazenave

引用次数: 23

Suspenser: A Story Generation System for Suspense 悬疑:悬念的故事生成系统

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2323894

Yun-Gyung Cheong, R. Young

引用次数: 51

Multiple Opponent Optimization of Prisoner’s Dilemma Playing Agents 囚徒困境博弈主体的多对手优化

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2326012

D. Ashlock, J. A. Brown, P. Hingston

{"title":"Multiple Opponent Optimization of Prisoner’s Dilemma Playing Agents","authors":"D. Ashlock, J. A. Brown, P. Hingston","doi":"10.1109/TCIAIG.2014.2326012","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2326012","url":null,"abstract":"Agents for playing iterated prisoner's dilemma are commonly trained using a coevolutionary system in which a player's score against a selection of other members of an evolving population forms the fitness function. In this study we examine instead a version of evolutionary iterated prisoner's dilemma in which an agent's fitness is measured as the average score it obtains against a fixed panel of opponents called an examination board. The performance of agents trained using examination boards is compared against agents trained in the usual coevolutionary fashion. This includes assessing the relative competitive ability of players evolved with evolution and coevolution. The difficulty of several experimental boards as optimization problems is compared. A number of new types of strategies are introduced. These include sugar strategies which can be exploited with some difficulty and treasure hunt strategies which have multiple trapping states with different levels of exploitability. The degree to which strategies trained with different examination boards produce different agents is investigated using fingerprints.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"7 1","pages":"53-65"},"PeriodicalIF":0.0,"publicationDate":"2015-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2326012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592052","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

Past Our Prime: A Study of Age and Play Style Development in Battlefield 3 《战地3》的年龄和游戏风格发展研究

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2015-01-20 DOI: 10.1109/TCIAIG.2015.2393433

S. Tekofsky, P. Spronck, M. Goudbeek, A. Plaat, Jaap van den Herik

引用次数: 13

Self-Adaptation of Playing Strategies in General Game Playing 一般博弈中博弈策略的自适应

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2014-12-01 DOI: 10.1109/TCIAIG.2013.2275163

M. Świechowski, J. Mańdziuk

{"title":"Self-Adaptation of Playing Strategies in General Game Playing","authors":"M. Świechowski, J. Mańdziuk","doi":"10.1109/TCIAIG.2013.2275163","DOIUrl":"https://doi.org/10.1109/TCIAIG.2013.2275163","url":null,"abstract":"The term general game playing (GGP) refers to a subfield of AI which aims at developing agents able to effectively play many games from a particular class (finite, deterministic). It is also the name of the annual competition proposed by Stanford Logic Group at Stanford University (Stanford, CA, USA), which provides a framework for testing and evaluating GGP agents. In this paper, we present our GGP player which managed to win four out of seven games in the 2012 preliminary round and advanced to the final phase. Our system (named MINI-Player) relies on a pool of playing strategies and autonomously picks the ones which seem to be best suited to a given game. The chosen strategies are combined with one another and incorporated into the upper confidence bounds applied to trees (UCT) algorithm. The effectiveness of our player is evaluated on a set of games from the 2012 GGP Competition as well as a few other, single-player games. The paper discusses the efficacy of proposed playing strategies and evaluates the mechanism of their switching. The proposed idea of dynamically assigning search strategies during play is both novel and promising.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"6 1","pages":"367-381"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2013.2275163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62591104","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: 64