IEEE Transactions on Computational Intelligence and AI in Games最新文献_第5页

Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner’s Dilemma Tournaments 利用进化模型在迭代囚徒困境竞赛中获胜

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-09-01 DOI: 10.1109/TCIAIG.2015.2439061

M. Gaudesi, Elio Piccolo, Giovanni Squillero, A. Tonda

{"title":"Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner’s Dilemma Tournaments","authors":"M. Gaudesi, Elio Piccolo, Giovanni Squillero, A. Tonda","doi":"10.1109/TCIAIG.2015.2439061","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2439061","url":null,"abstract":"The iterated prisoner's dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent's behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm; at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact nondeterministic finite state machines; they are extremely efficient in predicting opponents' replies, without being completely correct by necessity. Experimental results show that such a player is able to win several one-to-one games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"27 1","pages":"288-300"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2439061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593065","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: 10

Petalz: Search-Based Procedural Content Generation for the Casual Gamer Petalz:面向休闲玩家的基于搜索的程序内容生成

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-09-01 DOI: 10.1109/TCIAIG.2015.2416206

S. Risi, J. Lehman, David B. D'Ambrosio, Ryan Hall, Kenneth O. Stanley

{"title":"Petalz: Search-Based Procedural Content Generation for the Casual Gamer","authors":"S. Risi, J. Lehman, David B. D'Ambrosio, Ryan Hall, Kenneth O. Stanley","doi":"10.1109/TCIAIG.2015.2416206","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2416206","url":null,"abstract":"The impact of game content on the player experience is potentially more critical in casual games than in competitive games because of the diminished role of strategic or tactical diversions. Interestingly, until now procedural content generation (PCG) has nevertheless been investigated almost exclusively in the context of competitive, skills-based gaming. This paper therefore opens a new direction for PCG by placing it at the center of an entirely casual flower-breeding game platform called Petalz. That way, the behavior of players and their reactions to different game mechanics in a casual environment driven by PCG can be investigated. In particular, players in Petalz can: 1) trade their discoveries in a global marketplace; 2) respond to an incentive system that awards diversity; and 3) generate real-world 3-D replicas of their evolved flowers. With over 1900 registered online users and 38 646 unique evolved flowers, Petalz showcases the potential for PCG to enable these kinds of casual game mechanics, thus paving the way for continued innovation with PCG in casual gaming.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"244-255"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2416206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592500","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: 46

Time Management for Monte Carlo Tree Search 蒙特卡洛树搜索的时间管理

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-09-01 DOI: 10.1109/TCIAIG.2015.2443123

Hendrik Baier, M. Winands

{"title":"Time Management for Monte Carlo Tree Search","authors":"Hendrik Baier, M. Winands","doi":"10.1109/TCIAIG.2015.2443123","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2443123","url":null,"abstract":"Monte Carlo Tree Search (MCTS) is a popular approach for tree search in a variety of games. While MCTS allows for fine-grained time control, not much has been published on time management for MCTS programs under tournament conditions. This paper first investigates the effects of various time-management strategies on playing strength in the challenging game of Go. A number of domain-independent strategies are then tested in the domains Connect-4, Breakthrough, Othello, and Catch the Lion. We consider strategies taken from the literature as well as newly proposed and improved ones. Strategies include both semi-dynamic strategies that decide about time allocation for each search before it is started, and dynamic strategies that influence the duration of each move search while it is already running. Furthermore, we analyze the effects of time management strategies on the distribution of time over the moves of an average game, allowing us to partly explain their performance. In the experiments, the domain-independent strategy STOP provides a significant improvement over the state of the art in Go, and is the most effective time management strategy tested in all five domains.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"66 1","pages":"301-314"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2443123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593125","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

Specialization of a UCT-Based General Game Playing Program to Single-Player Games 基于uct的通用游戏程序对单人游戏的专门化

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-09-01 DOI: 10.1109/TCIAIG.2015.2391232

M. Świechowski, J. Mańdziuk, Y. Ong

{"title":"Specialization of a UCT-Based General Game Playing Program to Single-Player Games","authors":"M. Świechowski, J. Mańdziuk, Y. Ong","doi":"10.1109/TCIAIG.2015.2391232","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2391232","url":null,"abstract":"General game playing (GGP) aims at designing autonomous agents capable of playing any game within a certain genre, without human intervention. GGP agents accept the rules, which are written in the logic-based game definition language (GDL) and unknown to them beforehand, at runtime. The state-of-the-art players use Monte Carlo tree search (MCTS) together with the upper confidence bounds applied to trees (UCT) method. In this paper, we discuss several enhancements to GGP players geared towards more effective playing of single-player games within the MCTS/UCT framework. The main proposed improvements include introduction of a collection of lightweight policies which can be used for guiding the MCTS and a GGP-friendly way of using transposition tables. We have tested our base player and a specialized version of it for single-player games in a series of experiments using ten single-player games of various complexity. It is clear from the results that the optimized version of the player achieves significantly better performance. Furthermore, in the same set of tests against publicly available version of CadiaPlayer, one of the strongest GGP agents, the results are also favorable to the enhanced version of our player.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"218-228"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2391232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592542","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

Elicitation of Strategies in Four Variants of a Round-Robin Tournament: The Case of Goofspiel 循环赛四种变体的策略引出:以Goofspiel为例

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-09-01 DOI: 10.1109/TCIAIG.2014.2377250

M. Dror, G. Kendall, A. Rapoport

引用次数: 1

Multistage Temporal Difference Learning for 2048-Like Games 面向2048类游戏的多阶段时间差异学习

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-06-23 DOI: 10.1109/TCIAIG.2016.2593710

Kun-Hao Yeh, I-Chen Wu, Chu-Hsuan Hsueh, Chia-Chuan Chang, Chao-Chin Liang, Chiang Han

{"title":"Multistage Temporal Difference Learning for 2048-Like Games","authors":"Kun-Hao Yeh, I-Chen Wu, Chu-Hsuan Hsueh, Chia-Chuan Chang, Chao-Chin Liang, Chiang Han","doi":"10.1109/TCIAIG.2016.2593710","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2593710","url":null,"abstract":"Szubert and Jaśkowski successfully used temporal difference (TD) learning together with n -tuple networks for playing the game 2048. However, we observed a phenomenon that the programs based on TD learning still hardly reach large tiles. In this paper, we propose multistage TD (MS-TD) learning, a kind of hierarchical reinforcement learning method, to effectively improve the performance for the rates of reaching large tiles, which are good metrics to analyze the strength of 2048 programs. Our experiments showed significant improvements over the one without using MS-TD learning. Namely, using 3-ply expectimax search, the program with MS-TD learning reached 32768-tiles with a rate of 18.31%, while the one with TD learning did not reach any. After further tuned, our 2048 program reached 32768-tiles with a rate of 31.75% in 10,000 games, and one among these games even reached a 65536-tiles, which is the first ever reaching a 65536-tiles to our knowledge. In addition, MS-TD learning method can be easily applied to other 2048-like games, such as Threes. Based on MS-TD learning, our experiments for Threes also demonstrated similar performance improvement, where the program with MS-TD learning reached 6144-tiles with a rate of 7.83%, while the one with TD learning only reached 0.45%.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"9 1","pages":"369-380"},"PeriodicalIF":0.0,"publicationDate":"2016-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2593710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593103","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: 21

Angry-HEX: An Artificial Player for Angry Birds Based on Declarative Knowledge Bases Angry- hex:基于陈述性知识库的《愤怒的小鸟》人工玩家

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-06-01 DOI: 10.1109/TCIAIG.2015.2509600

Francesco Calimeri, Michael Fink, Stefano Germano, Giovambattista Ianni, Christoph Redl, Anton Wimmer

引用次数: 36

Akbaba—An Agent for the Angry Birds AI Challenge Based on Search and Simulation akbaba——基于搜索和模拟的《愤怒的小鸟》AI挑战代理

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-06-01 DOI: 10.1109/TCIAIG.2015.2478703

S. Schiffer, Maxim Jourenko, G. Lakemeyer

引用次数: 8

Guest Editorial: Physics-Based Simulation Games 嘉宾评论:基于物理的模拟游戏

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-06-01 DOI: 10.1109/TCIAIG.2016.2571560

Jochen Renz, R. Miikkulainen, Nathan R Sturtevant, M. Winands

引用次数: 6

A Straight Approach to Planning for 14.1 Billiards 14.1台球的直接规划方法

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-06-01 DOI: 10.1109/TCIAIG.2015.2462335

J. Landry, J. Dussault, É. Beaudry

引用次数: 3