IEEE Transactions on Computational Intelligence and AI in Games最新文献_第2页

Ahura: A Heuristic-Based Racer for the Open Racing Car Simulator Ahura:开放赛车模拟器的启发式赛车手

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-09-01 DOI: 10.1109/TCIAIG.2016.2565661

M. Bonyadi, Z. Michalewicz, Samadhi Nallaperuma, F. Neumann

{"title":"Ahura: A Heuristic-Based Racer for the Open Racing Car Simulator","authors":"M. Bonyadi, Z. Michalewicz, Samadhi Nallaperuma, F. Neumann","doi":"10.1109/TCIAIG.2016.2565661","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2565661","url":null,"abstract":"Designing automatic drivers for car racing is an active field of research in the area of robotics and artificial intelligence. A controller called Ahura (a heuristic-based racer) for the open racing car simulator is proposed in this paper. Ahura includes five modules, namely steer controller, speed controller, opponent manager, dynamic adjuster, and stuck handler. These modules have 23 parameters all together that are tuned using an evolutionary strategy for a particular car to ensure fast and safe drive on different tracks. These tuned parameters are further modified by the dynamic adjuster module during the run according to the width, friction, and dangerous zones of the track. The dynamic adjustment enables Ahura to decide on-the-fly based on the current situation; hence, it eliminates the need for prior knowledge about the characteristics of the track. The driving performance of Ahura is compared with other state-of-the-art controllers on 40 tracks when they drive identical cars. Our experiments indicate that Ahura performs significantly better than other controllers in terms of damage and completion time especially on complex tracks (road tracks). Also, experiments show that the overtaking strategy of Ahura is safer and more effective compared to other controllers.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"290-304"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2565661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41760776","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: 7

Product Propagation: A Backup Rule Better Than Minimaxing? 产品传播:比最小化更好的备份规则?

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-06-01 DOI: 10.1109/TCIAIG.2015.2508966

H. Kaindl, H. Horacek, A. Scheucher

{"title":"Product Propagation: A Backup Rule Better Than Minimaxing?","authors":"H. Kaindl, H. Horacek, A. Scheucher","doi":"10.1109/TCIAIG.2015.2508966","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2508966","url":null,"abstract":"There is a gap between theory and practice regarding the assessment of minimaxing versus product propagation. The use of minimaxing in real programs for certain two-player games like chess is more or less ubiquitous, due to the substantial search space reductions enabled by several pruning algorithms. In stark contrast, some theoretical work supported the view that product propagation could be a viable alternative, or even superior on theoretical grounds. In fact, these rules have different conceptual problems. While minimaxing treats heuristic values as true values, product propagation interprets them as independent probabilities. So, which is the better rule for backing up heuristic values in game trees, and under which circumstances? We present a systematic analysis and results of simulation studies that compare these backup rules in synthetic trees with properties found in certain real game trees, for a variety of situations with characteristic properties. Our results show yet unobserved complementary strengths in their respective capabilities, depending on the size of node score changes (“quiet” versus “nonquiet” positions), and on the degree of advantage of any player over the opponent. In particular, exhaustive analyses for shallow depths show that product propagation can indeed be better than minimaxing when both approaches search to the same depth, especially for making decisions from a huge amount of alternatives, where deep searches are still prohibitive. However, our results also provide some justification for the more or less ubiquitous use of minimaxing in chess programs, where deep searches prevail and the pruning algorithms available for minimaxing make the difference.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"109-122"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2508966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44552368","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: 3

The ANGELINA Videogame Design System—Part I 安吉丽娜电子游戏设计系统——上

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-06-01 DOI: 10.1109/TCIAIG.2016.2520256

Michael Cook, S. Colton, J. Gow

{"title":"The ANGELINA Videogame Design System—Part I","authors":"Michael Cook, S. Colton, J. Gow","doi":"10.1109/TCIAIG.2016.2520256","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2520256","url":null,"abstract":"Automatically generating content for videogames has long been a staple of game development and the focus of much successful research. Such forays into content generation usually concern themselves with producing a specific game component, such as a level design. This has proven a rich and challenging area of research, but in focusing on creating separate parts of a larger game, we miss out on the most challenging and interesting aspects of game development. By expanding our scope to the automated design of entire games, we can investigate the relationship between the different creative tasks undertaken in game development, tackle the higher level creative challenges of game design, and ultimately build systems capable of much greater novelty, surprise, and quality in their output. This paper, the first in a series of two, describes two case studies in automating game design, proposing cooperative coevolution as a useful technique to use within systems that automate this process. We show how this technique allows essentially separate content generators to produce content that complements each other. We also describe systems that have used this to design games with subtle emergent effects. After introducing the technique and its technical basis in this paper, in the second paper in the series we discuss higher level issues in automated game design, such as potential overlap with computational creativity and the issue of evaluation.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"192-203"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2520256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48663047","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: 52

HoningStone: Building Creative Combos With Honing Theory for a Digital Card Game HoningStone:基于珩磨理论创造一款数字纸牌游戏的创造性组合

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-06-01 DOI: 10.1109/TCIAIG.2016.2536689

L. F. Góes, Alysson Ribeiro da Silva, João Saffran, Alvaro Amorim, Celso França, Tiago Zaidan, Bernardo M. P. Olímpio, L. O. Alves, Hugo Morais, Shirley Luana, Carlos Martins

{"title":"HoningStone: Building Creative Combos With Honing Theory for a Digital Card Game","authors":"L. F. Góes, Alysson Ribeiro da Silva, João Saffran, Alvaro Amorim, Celso França, Tiago Zaidan, Bernardo M. P. Olímpio, L. O. Alves, Hugo Morais, Shirley Luana, Carlos Martins","doi":"10.1109/TCIAIG.2016.2536689","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2536689","url":null,"abstract":"In recent years, online digital games have left behind the status of entertainment sources to become also professional electronic sports. Worldwide championships offer prizes up to millions of dollars for the best competitors and/or teams among different game categories such as digital collectible card games (DCCG), multiplayer online battle arena, etc. Hearthstone, by Blizzard Entertainment, is a DCCG that has an increasing number of players up to the millions. In this game, individual players compete in one-versus-one matches in alternating turns, until a player is defeated. The greatest challenge in this game is to build a deck of cards and a strategy to combine these cards in order to be competitive against other players without a priori knowledge about their decks and strategies. This is a daunting task that requires deep knowledge of each existing card and great amount of creativity to surprise adversaries in this very adaptive environment. This paper presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. Our experimental results show that HoningStone can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"204-209"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2536689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46636191","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: 17

EvoCommander: A Novel Game Based on Evolving and Switching Between Artificial Brains EvoCommander:一款基于人工大脑之间进化和切换的新颖游戏

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-06-01 DOI: 10.1109/TCIAIG.2016.2535416

Daniel Jallov, S. Risi, J. Togelius

{"title":"EvoCommander: A Novel Game Based on Evolving and Switching Between Artificial Brains","authors":"Daniel Jallov, S. Risi, J. Togelius","doi":"10.1109/TCIAIG.2016.2535416","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2535416","url":null,"abstract":"Neuroevolution [i.e., evolving artificial neural networks (ANNs) through evolutionary algorithms] has shown promise in evolving agents and robot controllers, which display complex behaviors and can adapt to their environments. These properties are also relevant to video games, since they can increase their longevity and replayability. However, the design of most current games precludes the use of any techniques which might yield unpredictable or even open-ended results. This paper describes the game EvoCommander, with the goal to further demonstrate the potential of neuroevolution in games. In EvoCommander the player incrementally evolves an arsenal of ANN-controlled behaviors (e.g., ranged attack, flee, etc.) for a simple robot that has to battle other player and computer controlled robots. The game introduces the novel game mechanic of “brain switching,” selecting which evolved neural network is active at any point during battle. Results from playtests indicate that brain switching is a promising new game mechanic, leading to players employing interesting different strategies when training their robots and when controlling them in battle.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"181-191"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2535416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49251652","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: 16

A Pattern Mining Approach to Study Strategy Balance in RTS Games RTS游戏策略平衡研究的模式挖掘方法

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-06-01 DOI: 10.1109/TCIAIG.2015.2511819

G. Bosc, Philip Tan, Jean-François Boulicaut, Chedy Raïssi, Mehdi Kaytoue-Uberall

引用次数: 16

The ANGELINA Videogame Design System—Part II 安吉莉娜电子游戏设计系统(二

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-06-01 DOI: 10.1109/TCIAIG.2016.2520305

Michael Cook, S. Colton, J. Gow

{"title":"The ANGELINA Videogame Design System—Part II","authors":"Michael Cook, S. Colton, J. Gow","doi":"10.1109/TCIAIG.2016.2520305","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2520305","url":null,"abstract":"Procedural content generation is generally viewed as a means to an end—a tool employed by designers to overcome technical problems or achieve a particular design goal. When we move from generating single parts of games to automating the entirety of their design, however, we find ourselves facing a far wider and more interesting set of problems than mere generation. When the designer of a game is a piece of software, we face questions about what it means to be a designer, about computational creativity, and about how to assess the growth of these automated game designers and the value of their output. Answering these questions can lead to new ideas in how to generate content procedurally, and produce systems that can further the cutting edge of game design. This paper describes work done to take an automated game designer and advance it towards being a member of a creative community. We outline extensions made to the system to give it more autonomy and creative independence, in order to strengthen claims that the software is acting creatively. We describe and reflect upon the software’s participation in the games community, including entering two game development contests, and show the opportunities and difficulties of such engagement. We consider methods for evaluating automated game designers as creative entities, and underline the need for automated game design to be a major frontier in future games research.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"254-266"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2520305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42834445","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: 40

Only-One-Victor Pattern Learning in Computer Go 计算机围棋中的唯一胜利者模式学习

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-03-01 DOI: 10.1109/TCIAIG.2015.2504108

Jiao Wang, Chenjun Xiao, Tan Zhu, Chu-Hsuan Hsueh, Wen-Jie Tseng, I-Chen Wu

{"title":"Only-One-Victor Pattern Learning in Computer Go","authors":"Jiao Wang, Chenjun Xiao, Tan Zhu, Chu-Hsuan Hsueh, Wen-Jie Tseng, I-Chen Wu","doi":"10.1109/TCIAIG.2015.2504108","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2504108","url":null,"abstract":"Automatically acquiring domain knowledge from professional game records, a kind of pattern learning, is an attractive and challenging issue in computer Go. This paper proposes a supervised learning method, by introducing a new generalized Bradley-Terry model, named Only-One-Victor, to learn patterns from game records. Basically, our algorithm applies the same idea with Elo rating algorithm, which considers each move in game records as a group of move patterns, and the selected move as the winner of a kind of competition among all groups on current board. However, being different from the generalized Bradley-Terry model for group competition used in Elo rating algorithm, Only-One-Victor model in our work simulates the process of making selection from a set of possible candidates by considering such process as a group of independent pairwise comparisons. We use a graph theory model to prove the correctness of Only-One-Victor model. In addition, we also apply the Minorization-Maximization (MM) to solve the optimization task. Therefore, our algorithm still enjoys many computational advantages of Elo rating algorithm, such as the scalability with high dimensional feature space. With the training set containing 115,832 moves and the same feature setting, the results of our experiments show that Only-One-Victor outperforms Elo rating, a well-known best supervised pattern learning method.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"88-102"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2504108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48691366","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: 1

Partition Search Revisited 重新访问分区搜索

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-03-01 DOI: 10.1109/TCIAIG.2015.2505240

Piotr Beling

{"title":"Partition Search Revisited","authors":"Piotr Beling","doi":"10.1109/TCIAIG.2015.2505240","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2505240","url":null,"abstract":"Partition search is a form of game search, proposed by Matthew L. Ginsberg in 1996, who wrote that the method “incorporates dependency analysis, allowing substantial reductions in the portion of the tree that needs to be expanded.” In this paper, some improvements of the partition search algorithm are proposed. The effectiveness of the most important extension we contribute, which we call local partition search, has been verified experimentally. The results obtained (which we present in the paper) show that using this extension, leads, in the case of bridge, to a significant reduction (almost by half) of the search tree size and calculation time. Another extension we proposed allows for more effective usage of the transposition table (using it to narrow the search window or by cutting more than one entry). Additionally, we contribute a formal proof of the correctness of all presented partition search variants. We draw conclusions from it about a possible generalization of partition search by making the definition of a partition system less restrictive. We also provide a formal definition of a partition system for the double dummy bridge.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"76-87"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2505240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45648152","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: 3

Automatic Classification of Player Complaints in Social Games 社交游戏中玩家抱怨的自动分类

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-03-01 DOI: 10.1109/TCIAIG.2015.2490339

Koray Balci, A. A. Salah

引用次数: 3