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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
Q2 Computer Science Pub Date : 2015-03-05 DOI: 10.1109/TCIAIG.2015.2401979
Emiliano G. Castro, M. Tsuzuki
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.
在线游戏市场中免费游戏和其他基于服务的商业模式的兴起给游戏发行商带来了通常与移动通信和信用卡市场相关的问题,尤其是用户流失问题。在这些市场中,预测模型一直被用于解决这一问题,在这些市场中,公司拥有大量关于其客户的人口统计、经济和行为数据,而在线游戏发行商通常只有行为数据。简单的时间序列特征表示方案(如RFM)可以仅基于在线游戏玩家的登录记录提供合理的预测模型,但可能没有充分挖掘这些数据的预测潜力。我们提出了一种频率分析方法,用于用户流失预测建模的登录记录特征表示。使用四种不同的方法将这些条目(来自真实数据)转换为固定长度的数据数组,然后将这些条目用作使用k近邻机器学习算法训练概率分类器的输入。然后使用预测性能指标对分类器进行评估和比较。其中一种方法,时频平面域分析,显示了令人满意的结果,能够在理论上增加20%以上的保留活动的利润比RFM方法。
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引用次数: 54
Job-Level Alpha-Beta Search 工作级别Alpha-Beta搜索
Q2 Computer Science 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
An approach called generic job-level (JL) search was proposed to solve computer game applications by dispatching jobs to remote workers for parallel processing. This paper applies JL search to alpha-beta search, and proposes a JL alpha-beta search (JL-ABS) algorithm based on a best-first search version of MTD(f). The JL-ABS algorithm is demonstrated by using it in an opening book analysis for Chinese chess. The experimental results demonstrated that JL-ABS reached a speed-up of 10.69 when using 16 workers in the JL system.
提出了一种通用作业级(JL)搜索方法,通过将作业分配给远程工作者并行处理来解决计算机游戏应用问题。本文将JL搜索应用于alpha-beta搜索,提出了一种基于MTD(f)的最佳优先搜索版本的JL alpha-beta搜索(JL- abs)算法。通过对中国象棋开卷分析,对JL-ABS算法进行了验证。实验结果表明,在JL系统中使用16个工人时,JL- abs的加速率达到10.69。
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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
Q2 Computer Science 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
Chinese Dark Chess is an old and very popular game in the Chinese culture sphere. This game is a stochastic game with symmetric hidden information. This paper reviews alpha-beta search with chance nodes and proposes heuristics on Chinese Dark Chess programs. We propose an application of nondeterministic Monte Carlo Tree Search with random nodes for tackling partial observation. The proposed methods were implemented in the program Diablo, which won four Chinese Dark Chess tournaments in TAAI 2011/2012, TCGA 2011/2012 computer game tournaments. Diablo also played hundreds of games with different human players and programs based on alpha-beta search. These results show that the nondeterministic MCTS equipped with our heuristics is promising for Chinese Dark Chess.
中国黑棋是一种古老而非常流行的游戏,在中国文化圈。这个博弈是一个具有对称隐藏信息的随机博弈。本文综述了带有机会节点的alpha-beta搜索,并提出了中国象棋程序的启发式算法。提出了一种带随机节点的不确定性蒙特卡罗树搜索方法用于处理部分观测。所提出的方法在《暗黑破坏神》程序中得到了实现,该程序在TAAI 2011/2012、TCGA 2011/2012电脑游戏锦标赛中获得了四次中国暗黑象棋比赛的冠军。《暗黑破坏神》还与不同的人类玩家和基于alpha-beta搜索的程序进行了数百场游戏。这些结果表明,采用我们的启发式算法的不确定性MCTS在中国象棋中是很有前途的。
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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
Q2 Computer Science Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2329770
M. Ersen, Sanem Sariel
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.
在本文中,我们介绍了一个通过观察事件序列来学习对象行为和交互的自动推理系统。我们使用一个现有的系统来学习对象的模型,并进一步扩展它来模拟更复杂的行为。此外,我们提出了一种基于时空推理的学习方法来推理对象之间的相互作用。通过学习获得的经验将被这些对象用于实现目标。我们以The Incredible Machine (TIM)游戏为主要测试平台来分析我们的系统。游戏教程是用来训练系统的。我们在四种不同的输入类型上分析推理系统的结果:关系知识库;空间信息;时间信息;以及来自环境的时空信息。我们的分析表明,如果提供了一个关于关系的知识库,大多数交互都是可以学习的。我们还证明,我们的学习方法结合了空间和时间信息,其结果与基于知识的方法接近。这是有希望的,因为收集时空信息不需要事先了解关系。我们对电子游戏领域的时空推理方法进行了二次分析,验证了我们方法的成功。
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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
Q2 Computer Science Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2309077
Jiaxing Song, Martin Müller
The game of Amazons is a modern board game with simple rules and nice mathematical properties. It has a high computational complexity. In 2001, the starting position on a 5 × 5 board was proven to be a first player win. The enhanced Amazons solver presented here extends previous work in the following five ways: by building more powerful endgame databases, including a new type of databases for so-called blocker territories, by improving the rules for computing bounds on complex game positions, by local search to find tighter local bounds, by using ideas from combinatorial game theory to find wins earlier, and by using a df-pn based solver. Using the improved solver, the starting positions for Amazons on the 4 × 5, 5 × 4, 4 × 6, 5 × 6, and 4 × 7 boards were shown to be first player wins, while 6 × 4 is a second player win. The largest proof, for the 5 × 6 board, is presented in detail.
亚马逊的游戏是一个现代棋盘游戏与简单的规则和良好的数学性质。它具有很高的计算复杂度。2001年,在5 × 5棋盘上的起始位置被证明是第一个玩家获胜。本文提出的增强型amazon解算器在以下五个方面扩展了以前的工作:通过构建更强大的终局数据库,包括所谓的阻塞区域的新型数据库,通过改进复杂博弈位置计算边界的规则,通过局部搜索找到更紧密的局部边界,通过使用组合博弈论的思想找到更早的胜利,以及通过使用基于df-pn的解算器。使用改进的解算器,亚马逊在4 × 5、5 × 4、4 × 6、5 × 6和4 × 7棋盘上的起始位置显示为第一个玩家获胜,而6 × 4是第二个玩家获胜。最大的证明,为5 × 6板,详细介绍。
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引用次数: 7
Sequential Halving Applied to Trees 适用于树的顺序减半
Q2 Computer Science Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2317737
T. Cazenave
Monte Carlo tree search (MCTS) is state of the art for multiple games and problems. The base algorithm currently used for MCTS is UCT. We propose an alternative MCTS algorithm: sequential halving applied to Trees (SHOT). It has multiple advantages over UCT: it spends less time in the tree, it uses less memory, it is parameter free, at equal time settings it beats UCT for a complex combinatorial game and it can be efficiently parallelized.
蒙特卡罗树搜索(MCTS)是解决多个游戏和问题的最先进的方法。目前用于MCTS的基本算法是UCT。我们提出了一种替代的MCTS算法:应用于树的顺序减半(SHOT)。它比UCT有很多优点:它在树中花费的时间更少,使用的内存更少,没有参数,在相同的时间设置下,它在复杂的组合游戏中优于UCT,并且它可以有效地并行化。
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引用次数: 23
Suspenser: A Story Generation System for Suspense 悬疑:悬念的故事生成系统
Q2 Computer Science Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2323894
Yun-Gyung Cheong, R. Young
Interactive storytelling has been receiving a growing attention from AI and game communities and a number of computational approaches have shown promises in generating stories for games. However, there has been little research on stories evoking specific cognitive and affective responses. The goal of the work we describe here is to develop a system that produces a narrative designed specifically to arouse suspense from its reader. Our approach attempts to create stories that manipulate the reader's suspense level by elaborating on the story structure that can influence the reader's narrative comprehension at a specific point in her reading. Adapting theories developed by cognitive psychologists, our approach uses a plan-based model of narrative comprehension to determine the final content of the story in order to manipulate the reader's suspense. In this paper, we describe our system implementation and empirical evaluations to test the efficacy of this system.
互动故事已经受到AI和游戏社区越来越多的关注,许多计算方法已经显示出为游戏生成故事的前景。然而,关于故事引发特定认知和情感反应的研究很少。我们在这里描述的工作的目标是开发一个系统,产生一个专门设计的叙事,以引起悬念的读者。我们的方法试图通过阐述故事结构来操纵读者的悬念水平,从而影响读者在阅读过程中特定时刻的叙事理解。根据认知心理学家提出的理论,我们的方法采用基于计划的叙事理解模型来确定故事的最终内容,以操纵读者的悬念。在本文中,我们描述了我们的系统实施和实证评估,以检验该系统的有效性。
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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
Q2 Computer Science Pub Date : 2015-03-01 DOI: 10.1109/TCIAIG.2014.2326012
D. Ashlock, J. A. Brown, P. Hingston
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.
玩迭代囚徒困境的代理通常使用一种共同进化系统进行训练,在这种系统中,玩家对进化群体中其他成员的选择得分形成适应度函数。在这项研究中,我们研究了进化迭代囚徒困境的一个版本,在这个版本中,一个主体的适合度是用它在一个被称为考试委员会的固定对手小组中获得的平均分来衡量的。使用考试板训练的代理的性能与通常的共同进化方式训练的代理进行比较。这包括评估随着进化和共同进化而进化的玩家的相对竞争能力。比较了几种实验板作为优化问题的难度。介绍了一些新的策略类型。其中包括具有一定难度的糖策略和具有不同可利用程度的多个陷阱状态的寻宝策略。使用指纹调查了不同考试委员会训练的策略产生不同代理的程度。
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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
Q2 Computer Science Pub Date : 2015-01-20 DOI: 10.1109/TCIAIG.2015.2393433
S. Tekofsky, P. Spronck, M. Goudbeek, A. Plaat, Jaap van den Herik
In recent decades, video games have come to appeal to people of all ages. The effect of age on how people play games is not fully understood. In this paper, we delve into the question how age relates to an individual's play style. “Play style” is defined as any (set of) patterns in game actions performed by a player. Based on data from 10 416 Battlefield 3 players, we found that age strongly correlates to how people start out playing a game (initial play style), and to how they change their play style over time (play style development). Our data shows three major trends: 1) correlations between age and initial play style peak around the age of 20; 2) performance decreases with age; and 3) speed of play decreases with age. The relationship between age and play style may be explained by the neurocognitive effects of aging: as people grow older, their cognitive performance decays, their personalities shift to a more conscientious style, and their gaming motivations become less achievement-oriented.
近几十年来,电子游戏吸引了各个年龄段的人。年龄对人们玩游戏方式的影响还没有被完全理解。在本文中,我们将深入探讨年龄与个人游戏风格之间的关系。“游戏风格”被定义为玩家在游戏中执行的任何(一组)行为模式。根据10416名《战地3》玩家的数据,我们发现年龄与人们开始玩游戏的方式(最初的游戏风格)以及他们随着时间的推移如何改变游戏风格(游戏风格发展)密切相关。我们的数据显示了三个主要趋势:1)年龄和初始游戏风格之间的相关性在20岁左右达到顶峰;2)性能随年龄增长而下降;3)游戏速度随着年龄的增长而下降。年龄和游戏风格之间的关系可以用衰老的神经认知效应来解释:随着人们年龄的增长,他们的认知表现会衰退,他们的个性会转向更认真的风格,他们的游戏动机也会变得不那么以成就为导向。
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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
Q2 Computer Science Pub Date : 2014-12-01 DOI: 10.1109/TCIAIG.2013.2275163
M. Świechowski, J. Mańdziuk
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.
通用游戏玩法(GGP)指的是AI的一个子领域,它旨在开发能够有效地玩特定类别(有限的,确定性的)许多游戏的代理。这也是斯坦福大学(Stanford, CA, USA)斯坦福逻辑小组提出的年度竞赛的名称,该竞赛为测试和评估GGP代理提供了一个框架。在本文中,我们介绍了我们的GGP选手在2012年的预赛中取得了七场比赛中的四场胜利,并进入了决赛阶段。我们的系统(名为MINI-Player)依赖于一系列游戏策略,并自主选择最适合特定游戏的策略。将选择的策略相互组合,并纳入应用于树的上置信区间(UCT)算法。我们的玩家的有效性是根据2012年GGP竞赛的一系列游戏以及其他一些单人游戏来评估的。本文讨论了所提出的博弈策略的有效性,并评估了它们的转换机制。在游戏过程中动态分配搜索策略的想法既新颖又有前途。
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引用次数: 64
期刊
IEEE Transactions on Computational Intelligence and AI in Games
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