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Multiobjective Monte Carlo Tree Search for Real-Time Games 实时游戏的多目标蒙特卡罗树搜索
Q2 Computer Science Pub Date : 2015-12-01 DOI: 10.1109/TCIAIG.2014.2345842
Diego Perez Liebana, Sanaz Mostaghim, Spyridon Samothrakis, S. Lucas
Multiobjective optimization has been traditionally a matter of study in domains like engineering or finance, with little impact on games research. However, action-decision based on multiobjective evaluation may be beneficial in order to obtain a high quality level of play. This paper presents a multiobjective Monte Carlo tree search algorithm for planning and control in real-time game domains, those where the time budget to decide the next move to make is close to 40 ms. A comparison is made between the proposed algorithm, a single-objective version of Monte Carlo tree search and a rolling horizon implementation of nondominated sorting evolutionary algorithm II (NSGA-II). Two different benchmarks are employed, deep sea treasure (DST) and the multiobjective physical traveling salesman problem (MO-PTSP). Using the same heuristics on each game, the analysis is focused on how well the algorithms explore the search space. Results show that the algorithm proposed outperforms NSGA-II. Additionally, it is also shown that the algorithm is able to converge to different optimal solutions or the optimal Pareto front (if achieved during search).
传统上,多目标优化是工程或金融等领域的研究课题,对游戏研究影响不大。然而,为了获得高质量的游戏水平,基于多目标评估的行动决策可能是有益的。本文提出了一种多目标蒙特卡罗树搜索算法,用于实时博弈域的规划和控制,当决策下一步行动的时间预算接近40毫秒时。将该算法与蒙特卡罗树搜索的单目标版本和滚动视界实现的非支配排序进化算法II (NSGA-II)进行了比较。本文采用了深海宝藏(DST)和多目标物理旅行商问题(mo - pstp)两种不同的基准。在每个游戏中使用相同的启发式,分析的重点是算法如何探索搜索空间。结果表明,该算法优于NSGA-II。此外,还表明该算法能够收敛到不同的最优解或最优帕累托前沿(如果在搜索过程中实现)。
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引用次数: 24
Automated Planning and Player Modeling for Interactive Storytelling 交互式故事叙述的自动规划和玩家建模
Q2 Computer Science Pub Date : 2015-12-01 DOI: 10.1109/TCIAIG.2014.2346690
A. Ramirez, V. Bulitko
Storytelling plays an important role in human life, from everyday communication to entertainment. Interactive storytelling (IS) offers its audience an opportunity to actively participate in the story being told, particularly in video games. Managing the narrative experience of the player is a complex process that involves choices, authorial goals and constraints of a given story setting (e.g., a fairy tale). Over the last several decades, a number of experience managers using artificial intelligence (AI) methods such as planning and constraint satisfaction have been developed. In this paper, we extend existing work and propose a new AI experience manager called player-specific automated storytelling (PAST), which uses automated planning to satisfy the story setting and authorial constraints in response to the player's actions. Out of the possible stories algorithmically generated by the planner in response, the one that is expected to suit the player's style best is selected. To do so, we employ automated player modeling. We evaluate PAST within a video-game domain with user studies and discuss the effects of combining planning and player modeling on the player's perception of agency.
讲故事在人类生活中扮演着重要的角色,从日常交流到娱乐。交互式故事叙述(IS)为用户提供了一个积极参与故事叙述的机会,特别是在电子游戏中。管理玩家的叙述体验是一个复杂的过程,涉及到选择、创作目标和给定故事背景的约束(如童话故事)。在过去的几十年里,许多使用人工智能(AI)方法(如规划和约束满足)的体验管理器已经被开发出来。在本文中,我们扩展了现有的工作,并提出了一种新的AI体验管理器,称为玩家特定的自动故事叙述(PAST),它使用自动计划来满足故事设置和作者约束,以响应玩家的行动。从计划者根据算法生成的可能故事中,选择最符合玩家风格的故事。为此,我们使用了自动玩家建模。我们通过用户研究来评估视频游戏领域中的PAST,并讨论将计划和玩家建模结合起来对玩家代理感知的影响。
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引用次数: 41
A Panorama of Artificial and Computational Intelligence in Games 游戏中的人工智能和计算智能全景图
Q2 Computer Science Pub Date : 2015-12-01 DOI: 10.1109/TCIAIG.2014.2339221
Georgios N. Yannakakis, J. Togelius
This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: 1) the dominant AI method(s) used under each area; 2) the relation of each area with respect to the end (human) user; and 3) the placement of each area within a human-computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.
本文试图对游戏中的人工智能和计算智能(AI/CI)领域进行高层次的概述,并特别提到该领域内不同的核心研究领域是如何相互联系和互动的,无论是实际的还是潜在的。我们确定了该领域的十个主要研究领域:NPC行为学习、搜索和规划、玩家建模、作为AI基准的游戏、程序内容生成、计算叙事、可信代理、AI辅助游戏设计、通用游戏人工智能和商业游戏中的AI。我们从三个关键角度来看待和分析这些领域:1)每个领域使用的主要人工智能方法;2)每个区域相对于最终(人类)用户的关系;3)每个区域在人机(玩家-游戏)互动视角中的位置。此外,对于每一个领域,我们考虑它如何通知或与其他领域相互作用;在我们发现存在或可能存在有意义的相互作用的情况下,我们描述这种相互作用的特征,并提供已发表研究的参考资料(如果有的话)。我们相信本文通过提供一个统一的概述,提高了对游戏AI/CI研究领域的当前性质及其核心领域之间相互依赖性的理解。我们还认为,对研究领域之间潜在相互作用的讨论为许多有趣的未来研究项目和未开发的子领域提供了一个指针。
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引用次数: 179
Distributed Monte Carlo Tree Search: A Novel Technique and its Application to Computer Go 分布式蒙特卡罗树搜索:一种新技术及其在计算机围棋中的应用
Q2 Computer Science Pub Date : 2015-12-01 DOI: 10.1109/TCIAIG.2014.2346997
L. Schaefers, M. Platzner
Monte Carlo tree search (MCTS) has brought about great success regarding the evaluation of stochastic and deterministic games in recent years. We present and empirically analyze a data-driven parallelization approach for MCTS targeting large HPC clusters with Infiniband interconnect. Our implementation is based on OpenMPI and makes extensive use of its RDMA based asynchronous tiny message communication capabilities for effectively overlapping communication and computation. We integrate our parallel MCTS approach termed UCT-Treesplit in our state-of-the-art Go engine Gomorra and measure its strengths and limitations in a real-world setting. Our extensive experiments show that we can scale up to 128 compute nodes and 2048 cores in self-play experiments and, furthermore, give promising directions for additional improvement. The generality of our parallelization approach advocates its use to significantly improve the search quality of a huge number of current MCTS applications.
蒙特卡洛树搜索(MCTS)近年来在随机和确定性博弈的评估方面取得了巨大的成功。我们提出并实证分析了一种针对具有Infiniband互连的大型HPC集群的MCTS数据驱动并行化方法。我们的实现基于OpenMPI,并广泛利用其基于RDMA的异步微消息通信功能来有效地重叠通信和计算。我们将称为UCT-Treesplit的并行MCTS方法整合到我们最先进的围棋引擎Gomorra中,并在现实世界中测量其优势和局限性。我们广泛的实验表明,我们可以在自玩实验中扩展到128个计算节点和2048个核心,此外,还为进一步改进提供了有希望的方向。我们的并行化方法的通用性提倡使用它来显著提高大量当前MCTS应用程序的搜索质量。
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引用次数: 16
On Learning From Game Annotations 从游戏注解中学习
Q2 Computer Science Pub Date : 2015-09-01 DOI: 10.1109/TCIAIG.2014.2332442
Christian Wirth, Johannes Furnkranz
Most of the research in the area of evaluation function learning is focused on self-play. However in many domains, like Chess, expert feedback is amply available in the form of annotated games. This feedback usually comes in the form of qualitative information because human annotators find it hard to determine precise utility values for game states. The goal of this work is to investigate inasmuch it is possible to leverage this qualitative feedback for learning an evaluation function for the game. To this end, we show how the game annotations can be translated into preference statements over moves and game states, which in turn can be used for learning a utility function that respects these preference constraints. We evaluate the resulting function by creating multiple heuristics based upon different sized subsets of the training data and compare them in a tournament scenario. The results showed that learning from game annotations is possible, but, on the other hand, our learned functions did not quite reach the performance of the original, manually tuned function of the Chess program. The reason for this failure seems to lie in the fact that human annotators only annotate “interesting” positions, so that it is hard to learn basic information, such as material advantage from game annotations alone.
评价函数学习领域的研究大多集中在自我游戏方面。然而在许多领域,如象棋,专家反馈以注释游戏的形式存在。这种反馈通常以定性信息的形式出现,因为人类注释者很难确定游戏状态的精确效用值。这项工作的目标是调查,因为有可能利用这种定性反馈来学习游戏的评估功能。为此,我们展示了如何将游戏注释转换为关于移动和游戏状态的偏好陈述,这反过来又可以用于学习尊重这些偏好约束的效用函数。我们通过基于不同大小的训练数据子集创建多个启发式方法来评估结果函数,并在锦标赛场景中对它们进行比较。结果表明,从游戏注释中学习是可能的,但是,另一方面,我们学习的函数并没有完全达到原始的、手动调整的国际象棋程序函数的性能。这种失败的原因似乎在于人类注释者只注释“有趣”的位置,因此很难从游戏注释中学习到基本信息,例如材料优势。
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引用次数: 21
Postprocessing Gameplay Metrics for Gameplay Performance Segmentation Based on Audiovisual Analysis 基于视听分析的游戏性能分割的后处理游戏参数
Q2 Computer Science Pub Date : 2015-09-01 DOI: 10.1109/TCIAIG.2014.2382718
Raphaël Marczak, G. Schott, P. Hanna
This paper introduces a new variant of gameplay metrics that further develops a set of processes that expand user-centered game testing practices capable of quantifying user experiences. The key goal of the method presented here is to widen the appeal and application of game metrics within research relevant to, and representative of the wider field of game studies. In doing so, we acknowledge that the interests of this research community is often focused on player experience and performance with a broad range of off-the-shelf games that have already been released to the public. In order to be able to include any PC game system within research (or audiovideo stream, e.g., YouTube walkthroughs) our approach comprises of a postprocessing method for analyzing player performance. Through exploiting the audiovisual streams that are transmitted to the player, this approach processes content activated and generated during play and is therefore representative of individual player's encounters with specific games.
本文介绍了一种新的游戏参数变体,它进一步开发了一套流程,扩展了以用户为中心的游戏测试实践,能够量化用户体验。这里所呈现的方法的关键目标是扩大游戏参数在相关研究中的吸引力和应用,并代表更广泛的游戏研究领域。在这样做的过程中,我们承认这个研究社区的兴趣通常集中在玩家的体验和表现上,并且已经向公众发布了大量现成的游戏。为了能够在研究中包含任何PC游戏系统(或音频视频流,例如YouTube攻览),我们的方法包括分析玩家表现的后处理方法。通过利用传输给玩家的视听流,这种方法处理在游戏过程中激活和生成的内容,因此代表了单个玩家与特定游戏的遭遇。
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引用次数: 6
The Age of Analytics 分析时代
Q2 Computer Science Pub Date : 2015-09-01 DOI: 10.1109/TCIAIG.2015.2467166
C. Bauckhage, Anders Drachen, Christian Thurau
The articles in this special section ddress various flavors of the diverse field of game analytics. It covers topics ranging from player profiling, behavioral prediction, metrics extraction from gameplay recordings, behavioral analysis, retention analysis, and more.
在这个特殊的章节中,我们将讨论游戏分析领域的各种风格。它涵盖的主题包括玩家分析、行为预测、从游戏玩法记录中提取参数、行为分析、留存率分析等等。
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引用次数: 11
Player Preference and Style in a Leading Mobile Card Game 手机纸牌游戏中的玩家偏好和风格
Q2 Computer Science Pub Date : 2015-09-01 DOI: 10.1109/TCIAIG.2014.2357174
P. Cowling, Sam Devlin, E. Powley, D. Whitehouse, Jeff Rollason
Tuning game difficulty prior to release requires careful consideration. Players can quickly lose interest in a game if it is too hard or too easy. Assessing how players will cope prior to release is often inaccurate. However, modern games can now collect sufficient data to perform large scale analysis post deployment and update the product based on these insights. AI Factory Spades is currently the top rated Spades game in the Google Play store. In collaboration with the developers, we have collected gameplay data from 27 592 games and statistics regarding wins/losses for 99 866 games using Google Analytics. Using the data collected, this study analyses the difficulty and behavior of an Information Set Monte Carlo Tree Search player we developed and deployed in the game previously. The methods of data collection and analysis presented in this study are generally applicable. The same workflow could be used to analyze the difficulty and typical player or opponent behavior in any game. Furthermore, addressing issues of difficulty or nonhuman-like opponents postdeployment can positively affect player retention.
在发行前调整游戏难度需要仔细考虑。如果游戏太难或太简单,玩家很快就会对游戏失去兴趣。在游戏发行前评估玩家的反应通常是不准确的。然而,现代游戏现在可以收集足够的数据,在部署后执行大规模分析,并基于这些见解更新产品。《AI Factory Spades》目前是b谷歌Play商店中排名最高的《Spades》游戏。通过与开发者合作,我们收集了27592款游戏的玩法数据,并使用谷歌Analytics收集了99866款游戏的输赢数据。利用收集到的数据,本研究分析了我们之前在游戏中开发和部署的信息集蒙特卡罗树搜索玩家的难度和行为。本研究的数据收集和分析方法具有普遍适用性。同样的工作流程也可以用于分析任何游戏中的难度和典型玩家或对手行为。此外,在部署后解决难度问题或非人类对手可以积极影响玩家留存率。
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引用次数: 16
Thinking Style and Team Competition Game Performance and Enjoyment 思维方式与团队竞赛游戏表现与享受
Q2 Computer Science Pub Date : 2015-08-10 DOI: 10.1109/TCIAIG.2015.2466240
Hao Wang, Hao-Tsung Yang, Chuen-Tsai Sun
Almost all current matchmaking systems for team competition games based on player skill ratings contain algorithms designed to create teams consisting of players at similar skill levels. However, these systems overlook the important factor of playing style. In this paper, we analyze how playing style affects enjoyment in team competition games, using a mix of Sternberg's thinking style theory and individual histories in the form of statistics from previous matches to categorize League of Legend (LoL) players. Data for approximately 64 000 matches involving 185 000 players were taken from the LoLBase website. Match enjoyment was considered low when games lasted for 26 min or less (the earliest possible surrender time). Results from statistical analyses indicate that players with certain playing styles were more likely to enhance both game enjoyment and team strength. We also used a neural network model to test the usefulness of playing style information in predicting match quality. It is our hope that these results will support the establishment of more efficient matchmaking systems.
目前几乎所有基于玩家技能等级的团队竞赛游戏匹配系统都包含了一些算法,这些算法旨在创建由技能水平相似的玩家组成的团队。然而,这些系统忽略了游戏风格这一重要因素。本文将运用Sternberg的思维风格理论和个人历史数据对《英雄联盟》(LoL)玩家进行分类,分析游戏风格对团队竞技游戏乐趣的影响。大约64000场比赛的数据来自LoLBase网站,涉及18.5万名球员。当游戏持续26分钟或更短(最早的投降时间)时,游戏乐趣就会降低。统计分析结果表明,具有特定比赛风格的球员更有可能提高比赛乐趣和团队实力。我们还使用神经网络模型来测试比赛风格信息在预测比赛质量方面的有用性。我们希望这些结果将有助于建立更有效的婚介系统。
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引用次数: 16
Using Behavior Objects to Manage Complexity in Virtual Worlds 使用行为对象来管理虚拟世界中的复杂性
Q2 Computer Science Pub Date : 2015-08-03 DOI: 10.1109/TCIAIG.2016.2528499
Martin Černý, T. Plch, M. Marko, Jakub Gemrot, Petr Ondrácek, C. Brom
The quality of high-level AI of nonplayer characters (NPCs) in commercial open-world games (OWGs) has been increasing during the past years. However, due to constraints specific to the game industry, this increase has been slow and it has been driven by larger budgets rather than adoption of new complex AI techniques. Most of the contemporary AI is still expressed as hard-coded scripts. The complexity and manageability of the script codebase is one of the key limiting factors for further AI improvements. In this paper, we address this issue. We present behavior objects (BO)—a general approach to development of NPC behaviors for large OWGs. BOs are inspired by object-oriented programming and extend the concept of smart objects. Our approach promotes encapsulation of data and code for multiple related behaviors in one place, hiding internal details and embedding intelligence in the environment. BOs are a natural abstraction of five different techniques that we have implemented to manage AI complexity in an upcoming AAA OWG. We report the details of the implementations in the context of behavior trees and the lessons learned during development. Our study should serve as an inspiration for AI architecture designers from both the academia and the industry.
在过去几年里,商业开放世界游戏(owg)中非玩家角色(npc)的高级AI质量一直在提高。然而,由于游戏行业的特定限制,这种增长一直很缓慢,它是由更大的预算驱动的,而不是采用新的复杂AI技术。大多数当代AI仍然以硬编码脚本的形式呈现。脚本代码库的复杂性和可管理性是限制AI进一步改进的关键因素之一。在本文中,我们解决了这个问题。我们提出了行为对象(BO)——一种用于开发大型owg中NPC行为的通用方法。BOs受面向对象编程的启发,扩展了智能对象的概念。我们的方法将多个相关行为的数据和代码封装在一个地方,隐藏内部细节并在环境中嵌入智能。bo是我们在即将到来的AAA OWG中实现的五种不同技术的自然抽象,用于管理AI复杂性。我们在行为树的上下文中报告实现的细节以及在开发过程中获得的经验教训。我们的研究应该对学术界和工业界的人工智能架构设计师起到启发作用。
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引用次数: 5
期刊
IEEE Transactions on Computational Intelligence and AI in Games
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