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PEARS: Physics extension and representation through semantics 通过语义的物理扩展和表示
Q2 Computer Science Pub Date : 2016-06-01 DOI: 10.1109/TCIAIG.2015.2505404
Benjamin Eckstein, Jean-Luc Lugrin, Dennis Wiebusch, Marc Erich Latoschik
Today's physics engines mainly simulate classical mechanics and rigid body dynamics, with some late advances also capable of simulating massive particle systems and some approximations of fluid dynamics. An accurate numerical simulation of complex nonmechanical processes in real time is beyond the state of the art in the respective fields. This paper illustrates an alternative approach to a purely numerical solution. It uses a semantic representation of physical properties and processes as well as a reasoning engine to model cause and effect between objects, based on their material properties. Classical collision detection is combined with semantic rules to model various physical processes, for example, in the areas of thermodynamics, electrodynamics, and fluid dynamics as well as chemical processes. Each process is broken down into fine-grained subprocesses capable of approximating continuous transitions with discretized state changes. Our system applies these high-level state descriptions to low-level value changes, which are directly mapped to a graphical representation of the scene. We demonstrate our framework's ability to support multiple complex, causally connected physical and chemical processes by simulating a Goldberg machine. Our performance benchmarks validate its scalability and potential application for entertainment or edutainment purposes.
今天的物理引擎主要模拟经典力学和刚体动力学,最近的一些进展也能够模拟大质量粒子系统和一些近似流体动力学。对复杂的非机械过程进行精确的实时数值模拟,在各自的领域都是目前最先进的。本文说明了纯数值解的另一种方法。它使用物理属性和过程的语义表示,以及一个推理引擎,根据物体的材料属性对物体之间的因果关系进行建模。经典的碰撞检测与语义规则相结合来模拟各种物理过程,例如,在热力学、电动力学、流体动力学以及化学过程等领域。每个过程被分解成细粒度的子过程,这些子过程能够近似于具有离散状态变化的连续转换。我们的系统将这些高级状态描述应用于低级值变化,这些变化直接映射到场景的图形表示。通过模拟Goldberg机器,我们展示了我们的框架支持多个复杂的、因果关联的物理和化学过程的能力。我们的性能基准验证了它的可扩展性和潜在的娱乐或教育目的的应用程序。
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引用次数: 3
ghost: A Combinatorial Optimization Framework for Real-Time Problems ghost:一个用于实时问题的组合优化框架
Q2 Computer Science Pub Date : 2016-05-26 DOI: 10.1109/TCIAIG.2016.2573199
Florian Richoux, Alberto Uriarte, Jean-François Baffier
This paper presents GHOST, a combinatorial optimization framework that a real-time strategy (RTS) AI developer can use to model and solve any problem encoded as a constraint satisfaction/optimization problem (CSP/COP). We show a way to model three different problems as a CSP/COP, using instances from the RTS game StarCraft as test beds. Each problem belongs to a specific level of abstraction (the target selection as reactive control problem, the wall-in as a tactics problem, and the build order planning as a strategy problem). In our experiments, GHOST shows good results computed within some tens of milliseconds. We also show that GHOST outperforms state-of-the-art constraint solvers, matching them on the resources allocation problem, a common combinatorial optimization problem.
本文介绍了GHOST,这是一个组合优化框架,实时策略(RTS) AI开发者可以使用它来建模和解决任何编码为约束满足/优化问题(CSP/COP)的问题。我们使用RTS游戏《星际争霸》中的实例作为测试平台,展示了一种将三个不同问题建模为CSP/COP的方法。每个问题都属于特定的抽象层次(目标选择是反应性控制问题,进驻墙是战术问题,构建顺序计划是战略问题)。在我们的实验中,GHOST在几十毫秒内就得到了很好的计算结果。我们还表明GHOST优于最先进的约束求解器,在资源分配问题(一个常见的组合优化问题)上与它们相匹配。
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引用次数: 10
Qualitative Physics in Angry Birds 《愤怒的小鸟》中的定性物理
Q2 Computer Science Pub Date : 2016-05-02 DOI: 10.1109/TCIAIG.2016.2561080
P. Walega, Michał Zawidzki, Tomasz Lechowski
In this paper, we present a program designed to successfully and autonomously play Angry Birds, which attempts to embrace motives of human players in their choices of targets they want to shoot at in a game play. The program comprises two modules: the representation module and the reasoning module. In the former, we introduce qualitative space representation that utilizes notions such as “to lie on,” “to lie to the right,” “to be a shelter of a target,” etc. The latter investigates how particular blocks of a structure behave once one of them has been hit. It includes two algorithms, namely vertical impact and horizontal impact. The first one is a novel method of investigating the behavior of complex structures after one of their constituent blocks gets hit. Namely, it predicts which elements of a structure fall if a supporting block gets destroyed. Horizontal impact, on the other hand, simulates force propagation between adjacent elements after one of them gets struck. We also describe experimental tests we have conducted in which Vertical Impact correctly predicted which blocks will fall in over 98% of investigated cases.
在本文中,我们设计了一个能够成功自主地玩《愤怒的小鸟》的程序,该程序试图在玩家选择游戏中想要射击的目标时融入人类玩家的动机。该程序包括两个模块:表示模块和推理模块。在前者中,我们引入了定性空间表征,利用诸如“躺在”、“向右躺”、“成为目标的庇护所”等概念。后者研究一旦其中一个被击中,结构的特定块会如何表现。它包括两种算法,即垂直冲击和水平冲击。第一个是研究复杂结构的一个组成块被击中后的行为的新方法。也就是说,它预测如果一个支撑块被破坏,结构的哪些元素会倒塌。另一方面,水平冲击模拟了相邻单元中一个被击中后的力传播。我们还描述了我们进行的实验测试,在这些测试中,垂直冲击正确预测了98%以上的调查案例中哪些块会掉下来。
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引用次数: 17
s-Birds Avengers: A Dynamic Heuristic Engine-Based Agent for the Angry Birds Problem 小鸟复仇者:基于动态启发式引擎的愤怒小鸟问题代理
Q2 Computer Science Pub Date : 2016-04-12 DOI: 10.1109/TCIAIG.2016.2553244
Sourish Dasgupta, Savan Vaghela, Vishwa Modi, Hitarth Kanakia
Angry Birds is a popular video game in which a set of birds has to perform sling shots (bird shots) so as to kill pigs that are protected by a structure composed of different building blocks. The fewer birds we use and the more blocks we destroy, the higher the score we achieve. AIBirds competition is an AI challenge where an intelligent bot has to be developed that plays the game without human intervention. In this paper, we describe the approach implemented in the bot, s-birds Avengers, that participated in ECAI AIBirds 2014. Heuristic techniques were designed to analyze unseen structures using various structural parameters and then to discover their vulnerable points using prior parameter learning training algorithm. The bot then uses this to decide where to hit the structure with the birds.
《愤怒的小鸟》是一款很受欢迎的电子游戏,在游戏中,一群小鸟必须使用弹弓射击(小鸟射击)来杀死由不同积木组成的结构所保护的猪。我们使用的鸟越少,破坏的砖块越多,我们获得的分数就越高。AIBirds比赛是一项人工智能挑战,必须开发一个智能机器人,在没有人工干预的情况下玩游戏。在本文中,我们描述了在参与ECAI AIBirds 2014的机器人s-birds Avengers中实现的方法。设计了启发式技术,利用不同的结构参数分析未知结构,然后利用先验参数学习训练算法发现结构的弱点。然后机器人用这个来决定在哪里用鸟击中结构。
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引用次数: 8
Visual Detection of Unknown Objects in Video Games Using Qualitative Stability Analysis 基于定性稳定性分析的电子游戏中未知物体的视觉检测
Q2 Computer Science Pub Date : 2016-04-01 DOI: 10.1109/TCIAIG.2015.2506741
X. Ge, Jochen Renz, Peng Zhang
Many current computer vision approaches for object detection can only detect objects that have been learned in advance. In this paper, we present a method that uses qualitative stability analysis to infer the existence of unknown objects in certain areas of the images based on gravity and stability of already detected objects. Our method recursively searches these areas for unknown objects until all detected objects form a stable structure or no new objects can be identified anymore. We evaluate our method using the popular video game Angry Birds. We only start with detecting the green pigs and are able to automatically identify and detect all essential game objects in all 400+ available levels. All objects can be accurately and reliably detected. Our method can be applied to other video games where objects obey gravity and are bound by polygons.
目前许多用于物体检测的计算机视觉方法只能检测到事先学习过的物体。在本文中,我们提出了一种基于重力和已经检测到的物体的稳定性,使用定性稳定性分析来推断图像中某些区域是否存在未知物体的方法。我们的方法递归地搜索这些区域的未知对象,直到所有检测到的对象形成一个稳定的结构或没有新的对象可以识别。我们使用流行的电子游戏《愤怒的小鸟》来评估我们的方法。我们只从检测绿猪开始,并能够自动识别和检测所有400多个可用关卡中的所有重要游戏对象。所有物体都能被准确可靠地检测到。我们的方法可以应用于其他电子游戏,其中物体服从重力并受多边形约束。
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引用次数: 12
A Distributed Agent for Computational Pool 计算池的分布式代理
Q2 Computer Science Pub Date : 2016-04-01 DOI: 10.1109/TCIAIG.2016.2549748
Christopher Archibald, Alon Altman, Y. Shoham
Games with continuous state and action spaces present unique challenges from an artificial intelligence (AI) viewpoint. Billiards, or pool, is one such domain that has been the focus of several research efforts aimed at designing AI agents to play successfully. Due to the continuous nature of the actions, it is natural to believe that the more time an agent has to investigate actions, the better it will perform. This paper gives a thorough description of a successful agent with a novel distributed architecture, designed for being able to grant further time for shot simulation and analysis through the utilization of many CPUs. A brief analysis of the distributed component of the agent is presented, as well as how much the extra time thus obtained contributed to its success, especially when compared to its other novel components. The described agent, CueCard, won the Computer Olympiad computational pool tournament held in 2008.
从人工智能(AI)的角度来看,具有连续状态和动作空间的游戏呈现出独特的挑战。台球,或台球,就是这样一个领域,已经成为几个研究的焦点,旨在设计人工智能代理成功地玩。由于行为的连续性,我们很自然地认为智能体调查行为的时间越长,它的表现就越好。本文给出了一个成功的智能体的详细描述,该智能体具有新颖的分布式架构,旨在通过使用多个cpu来为射击模拟和分析提供更多的时间。简要分析了代理的分布式组件,以及由此获得的额外时间对其成功的贡献,特别是与其他新组件相比。所描述的代理CueCard赢得了2008年举行的计算机奥林匹克计算池锦标赛。
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引用次数: 3
Evolving Effective Microbehaviors in Real-Time Strategy Games 即时策略游戏中不断进化的有效微行为
Q2 Computer Science Pub Date : 2016-03-22 DOI: 10.1109/TCIAIG.2016.2544844
Siming Liu, S. Louis, Christopher A. Ballinger
We investigate heuristic search algorithms to generate high-quality micromanagement in combat scenarios for real-time strategy (RTS) games. Macro- and micromanagement are two key aspects of RTS games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes and battles against equal numbers and types of opponent units or win even when outnumbered. In this paper, we use influence maps and potential fields as a basis representation to evolve short-term positioning and movement tactics. Unit microbehaviors in combat are compactly encoded into 14 parameters. A genetic algorithm evolves good microbehaviors by manipulating these 14 parameters. We compared the performance of our evolved ECSLBot with two other state-of-the-art bots, UAlbertaBot and Nova, on several skirmish scenarios in a popular RTS game StarCraft. The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. Further experiments show that the parameter values evolved in one scenario work well in other scenarios and that we can switch between preevolved parameter sets to perform well in unseen scenarios containing more than one type of opponent unit. We believe our representation and approach applied to each unit type of interest can result in effective microperformance against melee and ranged opponents and provides a viable approach toward complete RTS bots.
我们研究了启发式搜索算法,以在实时战略(RTS)游戏的战斗场景中生成高质量的微管理。宏观和微观管理是RTS游戏的两个关键元素。优秀的宏观能够帮助玩家收集更多资源并创造更多单位,而优秀的微操作则能够帮助玩家在面对相同数量和类型的对手时赢得战斗,或者在寡不敌众的情况下获胜。在本文中,我们使用影响图和势场作为基础表示来演化短期定位和移动策略。战斗中的单位微行为被编码成14个参数。遗传算法通过操纵这14个参数来进化出良好的微行为。我们将进化后的ECSLBot与另外两个最先进的bot (UAlbertaBot和Nova)在流行的RTS游戏《星际争霸》中的几个小冲突场景中进行了比较。结果表明,经遗传算法调整后的ECSLBot在放线效率、目标选择和逃跑方面均优于ualberbot和Nova。进一步的实验表明,在一个场景中进化的参数值在其他场景中也能很好地工作,并且我们可以在预先进化的参数集之间切换,以便在包含多个类型的对手单元的未知场景中表现良好。我们相信我们的表现和方法适用于每一种单位类型的兴趣,可以产生有效的微性能对抗近战和远程对手,并提供一个可行的方法来完成RTS机器人。
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引用次数: 14
Coevolving Robust Build-Order Iterative Lists for Real-Time Strategy Games 实时策略游戏的协同进化鲁棒构建顺序迭代列表
Q2 Computer Science Pub Date : 2016-03-22 DOI: 10.1109/TCIAIG.2016.2544817
Christopher A. Ballinger, S. Louis, Siming Liu
We investigate and develop a coevolutionary approach to finding strong, robust build orders for real-time strategy games. Which units to produce and the order in which to produce them is one important aspect of real-time strategy gameplay. In real-time strategy games, creating plans to address unit production problems are called “build orders.” Our research compares build orders produced from a coevolutionary algorithm, genetic algorithm (GA), and hill climber (HC) to exhaustive search. GAs find the strongest build orders, while coevolution produces more robust build orders than a genetic algorithm or HC. Case injection into the coevolutionary teachset and population can be used to bias coevolution into producing build orders that beat specific opponents or play like specific players, while maintaining robustness. Finally, in this paper, we extend our representation by adding branching and iteration to the build-action sequence and show that this more complex representation enables coevolution to find stronger build orders. We believe this study is a start toward a promising approach for creating strong, robust build orders for RTS games.
我们研究并开发了一种共同进化方法,为即时战略游戏寻找强大的构建顺序。生产哪些单位以及生产它们的顺序是实时策略玩法的一个重要方面。在即时战略游戏中,制定解决单位生产问题的计划被称为“建造命令”。我们的研究比较了协同进化算法、遗传算法(GA)和爬山算法(HC)与穷举搜索产生的构建顺序。GAs找到最强的构建顺序,而协同进化产生比遗传算法或HC更健壮的构建顺序。将案例注入到共同进化的教学集和群体中,可以使共同进化偏向于产生打败特定对手或像特定玩家一样的构建顺序,同时保持健壮性。最后,在本文中,我们通过向构建-操作序列添加分支和迭代来扩展我们的表示,并表明这种更复杂的表示使共同进化能够找到更强的构建顺序。我们相信这项研究是为RTS游戏创造强大的构建命令的开端。
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引用次数: 3
Solving a Complex Language Game by Using Knowledge-Based Word Associations Discovery 用基于知识的词关联发现解决一个复杂的语言游戏
Q2 Computer Science Pub Date : 2016-03-01 DOI: 10.1109/TCIAIG.2014.2355859
Pierpaolo Basile, M. Degemmis, P. Lops, G. Semeraro
“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players.
“断头台”是一种语言游戏,其目标是预测一个独特的单词,这个单词以某种方式与五个作为线索的单词联系在一起,这些单词通常彼此无关。人类玩家找到解决方案的能力取决于其文化背景的丰富程度。我们为这款游戏设计了一个人工玩家,基于一个大型知识库(游戏邦注:该知识库利用了网络上的多种资源,如维基百科),为系统提供了理解线索所需的文化和语言背景。系统的“大脑”是一种扩展激活算法,它开始处理线索,在知识库中找到线索与单词之间的关联,并计算出候选解决方案列表。在本文中,我们关注的问题是找到最有希望的候选解作为最终答案。我们通过寻找候选解和线索之间的关联的两种策略改进了传播算法。这些策略允许双向推理,并选择与线索联系最紧密的候选解。实验表明,该系统的性能与普通人类玩家相当。
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引用次数: 16
Predicting Opponent's Production in Real-Time Strategy Games With Answer Set Programming 基于答案集编程的实时策略游戏对手产出预测
Q2 Computer Science Pub Date : 2016-03-01 DOI: 10.1109/TCIAIG.2014.2365414
Marius Stanescu, Michal Čertický
The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent's actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player's partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 min seems to be surprisingly high, making the method useful in practice. Root-mean-square error grows only slowly with increasing prediction intervals-almost in a linear fashion.
即时战略(RTS)游戏的对抗特征是这一领域不确定性的主要来源之一。因为玩家对对手的行动缺乏确切的了解,所以他们需要一个合理的替代可能性及其可能性的表示。在这篇文章中,我们提出了一种方法来预测对手在一定时间内最可能产生的单位组合。我们采用了一种称为答案集编程的逻辑编程范式,因为它的语义非常适合不确定性和不完整知识的推理。与典型的纯概率方法相比,本文提出的方法考虑了游戏的背景知识,只考虑与游戏机制和玩家的部分观察相一致的组合。在《星际争霸:母巢之战》和《魔兽争霸III:冰封王座》游戏的不同阶段进行的实验表明,在1-3分钟的时间间隔内,该方法的预测精度似乎高得惊人,这使得该方法在实践中很有用。均方根误差随着预测间隔的增加而缓慢增长,几乎呈线性增长。
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引用次数: 21
期刊
IEEE Transactions on Computational Intelligence and AI in Games
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