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Fast Algorithm for Catching a Prey Quickly in Known and Partially Known Game Maps 在已知和部分已知的游戏地图中快速捕获猎物的快速算法
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2337889
Jorge A. Baier, A. Botea, Daniel Damir Harabor, Carlos Hernández
In moving target search, the objective is to guide a hunter agent to catch a moving prey. Even though in game applications maps are always available at developing time, current approaches to moving target search do not exploit preprocessing to improve search performance. In this paper, we propose MtsCopa, an algorithm that exploits precomputed information in the form of compressed path databases (CPDs), and that is able to guide a hunter agent in both known and partially known terrain. CPDs have previously been used in standard, fixed-target pathfinding but had not been used in the context of moving target search. We evaluated MtsCopa over standard game maps. Our speed results are orders of magnitude better than current state of the art. The time per individual move is improved, which is important in real-time search scenarios, where the time available to make a move is limited. Compared to state of the art, the number of hunter moves is often better and otherwise comparable, since CPDs provide optimal moves along shortest paths. Compared to previous successful methods, such as I-ARA*, our method is simple to understand and implement. In addition, we prove MtsCopa always guides the agent to catch the prey when possible.
在移动目标搜索中,目标是引导猎人捕捉移动的猎物。尽管在游戏应用程序中,地图在开发时总是可用的,但当前的移动目标搜索方法并没有利用预处理来提高搜索性能。在本文中,我们提出了MtsCopa算法,该算法利用压缩路径数据库(CPDs)形式的预计算信息,能够在已知和部分已知的地形中引导猎人代理。cpd先前已用于标准的固定目标寻路,但尚未用于移动目标搜索。我们在标准游戏地图上评估了MtsCopa。我们的速度结果比目前的技术水平好几个数量级。每个个体移动的时间得到了改善,这在实时搜索场景中很重要,在实时搜索场景中,移动的可用时间是有限的。与最先进的技术相比,猎人移动的数量通常更好,因为cpd提供了沿着最短路径的最佳移动。与以前成功的方法(如I-ARA*)相比,我们的方法易于理解和实现。此外,我们还证明了MtsCopa总是在可能的情况下引导agent捕捉猎物。
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引用次数: 11
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning 基于强化学习的第一人称射击游戏bot自适应射击
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2363042
F. Glavin, M. G. Madden
In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as nonplayer characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, “sixth sense” knowledge of human players' whereabouts and deterministic, scripted behaviors are some of the causes of this. We propose, however, that one of the biggest indicators of nonhumanlike behavior in these games can be found in the weapon shooting capability of the bot. Consistently perfect accuracy and “locking on” to opponents in their visual field from any distance are indicative capabilities of bots that are not found in human players. Traditionally, the bot is handicapped in some way with either a timed reaction delay or a random perturbation to its aim, which doesn't adapt or improve its technique over time. We hypothesize that enabling the bot to learn the skill of shooting through trial and error, in the same way a human player learns, will lead to greater variation in game-play and produce less predictable nonplayer characters. This paper describes a reinforcement learning shooting mechanism for adapting shooting over time based on a dynamic reward signal from the amount of damage caused to opponents.
在当前最先进的商业第一人称射击游戏中,计算机控制的机器人(也称为非玩家角色)通常很容易与人类控制的机器人区分开来。诸如导航失败、人类玩家行踪的“第六感”知识以及确定性、脚本化行为都是导致这种情况的一些原因。然而,我们认为这些游戏中最大的非人类行为指标之一是机器人的武器射击能力。始终如一的精准度和从任何距离“锁定”对手都是机器人的表现能力,这是人类玩家所没有的。传统上,机器人在某种程度上受到定时反应延迟或随机干扰的限制,这不能随着时间的推移而适应或改进其技术。我们假设让bot通过试错来学习射击技能,就像人类玩家学习的方式一样,将导致游戏玩法的更大变化,并产生更不可预测的非玩家角色。本文描述了一种强化学习射击机制,该机制基于对对手造成伤害的动态奖励信号,随着时间的推移适应射击。
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引用次数: 25
A Memory-Efficient Method for Fast Computation of Short 15-Puzzle Solutions 一种快速计算短15题解的高效内存方法
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2352255
I. Parberry
While the 15-puzzle has a long and interesting history dating back to the 1870s, it still continues to appear as apps on mobile devices and as minigames inside larger video games. We demonstrate a method for solving the 15-puzzle using only 4.7 MB of tables that on a million random instances was able to find solutions of 65.21 moves on average and 95 moves in the worst case in under a tenth of a millisecond per solution on current desktop computing hardware. These numbers compare favorably to the worst case upper bound of 80 moves and to the greedy algorithm published in 1995, which required 118 moves on average and 195 moves in the worst case.
虽然15谜题的历史可以追溯到19世纪70年代,但它仍然以移动设备上的应用程序和大型电子游戏中的迷你游戏的形式出现。我们演示了一种解决15谜题的方法,该方法仅使用4.7 MB的表,在一百万个随机实例上能够找到平均65.21步和最坏情况下95步的解决方案,在当前桌面计算硬件上,每个解决方案的时间不到十分之一毫秒。这些数字与最坏情况下的上限80步和1995年发布的贪婪算法相比是有利的,贪婪算法平均需要118步,最坏情况下需要195步。
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引用次数: 8
Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms 基于进化算法的个性化程序图生成集成方法
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2341665
W. Raffe, Fabio Zambetta, Xiaodong Li, Kenneth O. Stanley
In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual player's preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the player's preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.
在本文中,我们提出了集成多个进化过程的个性化程序内容生成(PCG)策略。在这种情况下,我们提供了一个具体的解决方案,即在自上而下的动作射击游戏中个性化游戏地图,以适应个人玩家的喜好。随着玩家市场的多样化,对个性化PCG的需求正在稳步增长,这使得设计一款能够适应广泛偏好和技能的游戏变得更加困难。在这里呈现的解决方案中,地图的几何形状和几何形状中的内容密度在不同的进化过程中呈现和生成,玩家的偏好通过互动进化和作为推荐系统的玩家模型的组合被捕获和利用。所有这些组件都被执行到一个测试平台游戏中,并通过一个无监督的公共实验进行实验。这个解决方案是根据一个可信的随机基线来检验的,这个基线与独立游戏开发者所执行的随机地图生成器相当。结果表明,整个系统获得了更好的评级,几何和内容进化过程正在探索更多的解决方案空间,玩家偏好模型的平均预测精度与现有推荐系统文献相当。此外,我们还将讨论如何将每个解决方案用于其他游戏类型和内容类型。
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引用次数: 15
Creating Autonomous Adaptive Agents in a Real-Time First-Person Shooter Computer Game 在实时第一人称射击电脑游戏中创建自主自适应代理
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2336702
D. Wang, A. Tan
Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount of research dealing with real-time computer games other than the traditional board games or card games. This paper illustrates how we create agents by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first-person shooter computer game called Unreal Tournament. Rewards used for learning are either obtained from the game environment or estimated using the temporal difference learning scheme. In this way, the agents are able to acquire proper strategies and discover the effectiveness of different weapons without any guidance or intervention. The experimental results show that our agents learn effectively and appropriately from scratch while playing the game in real-time. Moreover, with the previously learned knowledge retained, our agent is able to adapt to a different opponent in a different map within a relatively short period of time.
游戏是评估AI方法的良好测试平台。近年来,除了传统的桌面游戏或纸牌游戏之外,人们对实时电脑游戏进行了大量的研究。本文阐述了我们如何通过使用FALCON(一种执行强化学习的自组织神经网络)来创建代理,并玩一款著名的第一人称射击电脑游戏《虚幻竞技场》。用于学习的奖励要么从游戏环境中获得,要么使用时间差异学习方案估计。这样,agent就可以在没有任何指导和干预的情况下获得适当的策略,并发现不同武器的有效性。实验结果表明,我们的智能体在实时玩游戏的过程中,能够有效而恰当地从零开始学习。此外,由于保留了先前学习的知识,我们的智能体能够在相对较短的时间内适应不同地图上的不同对手。
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引用次数: 44
MCTS-Minimax Hybrids MCTS-Minimax混合动力车
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2366555
Hendrik Baier, M. Winands
Monte Carlo tree search (MCTS) is a sampling-based search algorithm that is state of the art in a variety of games. In many domains, its Monte Carlo rollouts of entire games give it a strategic advantage over traditional depth-limited minimax search with αβ pruning. These rollouts can often detect long-term consequences of moves, freeing the programmer from having to capture these consequences in a heuristic evaluation function. But due to its highly selective tree, MCTS runs a higher risk than full-width minimax search of missing individual moves and falling into traps in tactical situations. This paper proposes MCTS-minimax hybrids that integrate shallow minimax searches into the MCTS framework. Three approaches are outlined, using minimax in the selection/expansion phase, the rollout phase, and the backpropagation phase of MCTS. Without assuming domain knowledge in the form of evaluation functions, these hybrid algorithms are a first step towards combining the strategic strength of MCTS and the tactical strength of minimax. We investigate their effectiveness in the test domains of Connect-4, Breakthrough, Othello, and Catch the Lion, and relate this performance to the tacticality of the domains.
蒙特卡罗树搜索(MCTS)是一种基于采样的搜索算法,在各种游戏中都是最先进的。在许多领域,它的蒙特卡洛整个游戏的推出给了它一个战略优势比传统的深度限制极大极小搜索与αβ修剪。这些部署通常可以检测移动的长期结果,从而使程序员不必在启发式评估函数中捕获这些结果。但由于它的高度选择性树,MCTS比全宽度极小极大搜索有更高的风险,会丢失单个动作,并在战术情况下陷入陷阱。本文提出了一种将浅极大极小搜索整合到MCTS框架中的MCTS-minimax混合算法。概述了三种方法,即在MCTS的选择/扩展阶段、推出阶段和反向传播阶段使用极小最大值。这些混合算法不以评估函数的形式假设领域知识,是将MCTS的战略强度和极大极小的战术强度相结合的第一步。我们研究了它们在Connect-4、Breakthrough、Othello和Catch the Lion的测试域中的有效性,并将这种性能与这些域的战术性联系起来。
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引用次数: 22
Stronger Virtual Connections in Hex 十六进制中更强的虚拟连接
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2345398
Jakub Pawlewicz, R. Hayward, Philip Henderson, B. Arneson
For connection games such as Hex or Y or Havannah, finding guaranteed cell-to-cell connection strategies can be a computational bottleneck. In automated players and solvers, sets of such virtual connections are often found with Anshelevich's H-search algorithm: initialize trivial connections, and then repeatedly apply an AND-rule (for combining connections in series) and an OR-rule (for combining connections in parallel). We present FastVC Search, a new algorithm for finding such connections. FastVC Search is more effective than H-search when finding a representative set of connections quickly is more important than finding a larger set of connections slowly. We tested FastVC Search in an alpha-beta player Wolve, a Monte Carlo tree search player MoHex, and a proof number search implementation called Solver. It does not strengthen Wolve, but it significantly strengthens MoHex and Solver.
对于像Hex或Y或Havannah这样的连接游戏,找到有保证的单元到单元的连接策略可能是一个计算瓶颈。在自动玩家和求解器中,这种虚拟连接的集合通常是用Anshelevich的h搜索算法找到的:初始化平凡的连接,然后反复应用and规则(用于串联连接的组合)和or规则(用于并行连接的组合)。我们提出了FastVC搜索算法,这是一种寻找这种连接的新算法。当快速找到一组有代表性的连接比缓慢地找到一组更大的连接更重要时,FastVC搜索比h搜索更有效。我们在alpha-beta玩家Wolve、Monte Carlo树搜索玩家MoHex和证明数搜索实现Solver中测试了FastVC Search。它并没有强化《狼》,但却显著强化了《MoHex》和《Solver》。
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引用次数: 13
Equivalence Classes in Chinese Dark Chess Endgames 中国黑棋残局中的等价类
Q2 Computer Science Pub Date : 2015-06-01 DOI: 10.1109/TCIAIG.2014.2317832
Jr-Chang Chen, Ting-Yu Lin, Bo-Nian Chen, T. Hsu
Chinese Dark Chess, a nondeterministic two-player game, has not been studied thoroughly. State-of-the-art programs focus on using search algorithms to explore the probability behavior of flipping unrevealed pieces in the opening and the midgame phases. There has been comparatively little research on opening books and endgame databases, especially endgames with nondeterministic flips. In this paper, we propose an equivalence relation that classifies the complex piece relations between the material combinations of each player, and derive a partition for all such material combinations. The technique can be applied to endgame database compression to reduce the number of endgames that need to be constructed. As a result, the computation time and the size of endgame databases can be reduced substantially. Furthermore, understanding the piece relations facilitates the development of a well-designed evaluation function and enhances the search efficiency. In Chinese Dark Chess, the number of nontrivial material combinations comprised of only revealed pieces is 8 497 176, and the number that contain at least one unrevealed piece is 239 980 775 397. Under the proposed method, the compression rates of the above material combinations reach 28.93% and 42.52%, respectively; if the method is applied to endgames comprised of three to eight pieces, the compression rates reach 5.82% and 5.98%, respectively.
中国象棋是一种不确定的双人棋,目前还没有得到深入的研究。最先进的程序专注于使用搜索算法来探索在开局和游戏中期阶段翻转未暴露棋子的概率行为。关于开卷和残局数据库的研究相对较少,特别是具有不确定性的残局。在本文中,我们提出了一个等价关系,对每个玩家的材料组合之间的复杂块关系进行分类,并推导了所有这些材料组合的划分。该技术可以应用于终局数据库压缩,以减少需要构建的终局的数量。因此,计算时间和终端数据库的大小可以大大减少。此外,了解片段关系有助于开发设计良好的评价函数,提高了搜索效率。在中国黑棋中,仅由露出的棋子组成的非平凡材料组合的数量为8 497 176,包含至少一个未露出的棋子的数量为239 980 775 397。在该方法下,上述材料组合的压缩率分别达到28.93%和42.52%;如果将该方法应用于由3到8个棋子组成的终局,压缩率分别达到5.82%和5.98%。
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引用次数: 17
Detecting Predatory Behavior in Game Chats 在游戏聊天中检测掠夺行为
Q2 Computer Science Pub Date : 2015-04-21 DOI: 10.1109/TCIAIG.2015.2424932
Yun-Gyung Cheong, A. K. Jensen, Elin Rut Gudnadottir, Byung-Chull Bae, J. Togelius
While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStarPlanet, a massively multiplayer online game for children. The research described in this paper aimed to detect predatory behaviors in the chats using machine learning methods. In order to achieve a high accuracy on this task, extensive preprocessing was necessary. We describe three different strategies for data selection and preprocessing, and extensively compare the performance of different learning algorithms on the different data sets and features.
虽然游戏是一种受儿童欢迎的社交媒体,但这些儿童面临潜在性侵犯的真正风险。许多研究已经解决了这个问题,然而,之前的研究中使用的数据并不能很好地代表多人在线游戏中的真实聊天。为了解决这个问题,我们从儿童大型多人在线游戏《MovieStarPlanet》中获得了真实的聊天数据。本文描述的研究旨在使用机器学习方法检测聊天中的掠夺性行为。为了在这项任务中达到较高的精度,需要进行大量的预处理。我们描述了三种不同的数据选择和预处理策略,并广泛比较了不同学习算法在不同数据集和特征上的性能。
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引用次数: 20
An Analytic and Psychometric Evaluation of Dynamic Game Adaption for Increasing Session-Level Retention in Casual Games 动态游戏适应提高休闲游戏会话留存率的分析与心理测量学评价
Q2 Computer Science Pub Date : 2015-03-05 DOI: 10.1109/TCIAIG.2015.2410757
Brent E. Harrison, D. Roberts
This paper shows how game analytics can be used to dynamically adapt casual game environments in order to increase session-level retention. Our technique involves using game analytics to create an abstracted game analytic space to make the problem tractable. We then model player retention in this space and use these models to make guided changes to game analytics in order to bring about a targeted distribution of game states that will, in turn, influence player behavior. Experiments performed showed that the adaptive versions of two different casual games, Scrabblesque and Sidequest: The Game, were able to better fit a target distribution of game states while also significantly reducing the quitting rate compared to the nonadaptive version of the games. We showed that these gains were not coming at the cost of player experience by performing a psychometric evaluation in which we measured player intrinsic motivation and engagement with the game environments. In both cases, we showed that players playing the adaptive version of the games reported higher intrinsic motivation and engagement scores than players playing the nonadaptive version of the games.
本文展示了如何使用游戏分析来动态调整休闲游戏环境,从而提高会话级留存率。我们的技术包括使用游戏分析来创建一个抽象的游戏分析空间,使问题易于处理。然后,我们在这一领域对玩家留存率进行建模,并使用这些模型对游戏分析进行指导性改变,从而带来有针对性的游戏状态分布,从而影响玩家行为。实验表明,两款不同的休闲游戏《Scrabblesque》和《Sidequest: the Game》的自适应版本能够更好地适应游戏状态的目标分布,同时与非自适应版本的游戏相比,也显著降低了玩家的退出率。我们通过测量玩家内在动机和对游戏环境的沉浸度的心理评估,证明了这些收益并不是以牺牲玩家体验为代价的。在这两种情况下,我们发现玩自适应版本游戏的玩家比玩非自适应版本游戏的玩家报告了更高的内在动机和粘性分数。
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引用次数: 6
期刊
IEEE Transactions on Computational Intelligence and AI in Games
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