IEEE Transactions on Computational Intelligence and AI in Games最新文献_第3页

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

Opponent Modeling by Expectation–Maximization and Sequence Prediction in Simplified Poker 简化扑克中基于期望-最大化和序列预测的对手建模

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-03-01 DOI: 10.1109/TCIAIG.2015.2491611

Richard Mealing, J. Shapiro

引用次数: 18

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

A Hyperheuristic Methodology to Generate Adaptive Strategies for Games 生成游戏自适应策略的超启发式方法

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-03-01 DOI: 10.1109/TCIAIG.2015.2394780

Jiawei Li, G. Kendall

引用次数: 18

Creating Affective Autonomous Characters Using Planning in Partially Observable Stochastic Domains 在部分可观测随机域中使用规划创建情感自主角色

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2017-03-01 DOI: 10.1109/TCIAIG.2015.2494599

Xiangyang Huang, Shudong Zhang, Yuanyuan Shang, Wei-gong Zhang, Jie Liu

引用次数: 5