2013 IEEE Conference on Computational Inteligence in Games (CIG)最新文献_第4页

Production of various strategies and position control for Monte-Carlo Go — Entertaining human players 制作各种策略和位置控制的蒙特卡洛围棋娱乐人类玩家

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633625

Kokolo Ikeda, Simon Viennot

引用次数: 15

The impact of connection topology and agent size on cooperation in the iterated prisoner's dilemma 迭代囚徒困境中连接拓扑和智能体大小对合作的影响

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633611

Lee-Ann Barlow, D. Ashlock

{"title":"The impact of connection topology and agent size on cooperation in the iterated prisoner's dilemma","authors":"Lee-Ann Barlow, D. Ashlock","doi":"10.1109/CIG.2013.6633611","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633611","url":null,"abstract":"This study revisits earlier work, concerning the evolutionary trajectory of agents trained to play iterated prisoner's dilemma on a combinatorial graph. The impact of different connection topologies, used to mediate both the play of prisoner's dilemma and the flow of genes during selection and replacement, is examined. The variety of connection topologies, stored as combinatorial graphs, is revisited and the analysis tools used are substantially improved. A novel tool called the play profile summarizes the distribution of behaviors over multiple replicates of the basic evolutionary algorithm and through multiple evolutionary epochs. The impact of changing the number of states used to encode agents is also examined. Changing the combinatorial graph on which the population resides is found to yield statistically significant differences in the play profiles. Changing the number of states in agents is also found to produce statistically significant differences in behavior. The use of multiple epochs in analysis of agent behavior demonstrates that the distribution of behaviors changes substantially over the course of evolution. The most common pattern is for agents to move toward the cooperative state over time, but this pattern is not universal. Another clear trend is that agents implemented with more states are less cooperative.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124585311","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: 12

Behavior evolution in Tomb Raider Underworld 《古墓丽影:地下世界》中的行为进化

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633637

R. Sifa, Anders Drachen, C. Bauckhage, Christian Thurau, Alessandro Canossa

{"title":"Behavior evolution in Tomb Raider Underworld","authors":"R. Sifa, Anders Drachen, C. Bauckhage, Christian Thurau, Alessandro Canossa","doi":"10.1109/CIG.2013.6633637","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633637","url":null,"abstract":"Behavioral datasets from major commercial game titles of the “AAA” grade generally feature high dimensionality and large sample sizes, from tens of thousands to millions, covering time scales stretching into several years of real-time, and evolving user populations. This makes dimensionality-reduction methods such as clustering and classification useful for discovering and defining patterns in player behavior. The goal from the perspective of game development is the formation of behavioral profiles that provide actionable insights into how a game is being played, and enables the detection of e.g. problems hindering player progression. Due to its unsupervised nature, clustering is notably useful in cases where no prior-defined classes exist. Previous research in this area has successfully applied clustering algorithms to behavioral datasets from different games. In this paper, the focus is on examining the behavior of 62,000 players from the major commercial game Tomb Raider: Underworld, as it unfolds from the beginning of the game and throughout the seven main levels of the game. Where previous research has focused on aggregated behavioral datasets spanning an entire game, or conversely a limited slice or snapshot viewed in isolation, this is to the best knowledge of the authors the first study to examine the application of clustering methods to player behavior as it evolves throughout an entire game.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130123457","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: 38

The turing test track of the 2012 Mario AI Championship: Entries and evaluation 2012马里奥AI锦标赛的图灵测试赛道:参赛和评估

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633634

Noor Shaker, J. Togelius, Georgios N. Yannakakis, Likith Poovanna, Vinay Sudha Ethiraj, S. Johansson, R. Reynolds, Leonard Kinnaird-Heether, T. Schumann, M. Gallagher

引用次数: 38

Stacked calibration of off-policy policy evaluation for video game matchmaking 视频游戏配对非政策评估的堆叠校准

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633642

Eric Thibodeau-Laufer, Raul Chandias Ferrari, Li Yao, Olivier Delalleau, Yoshua Bengio

引用次数: 0

Exploration and analysis of the evolution of strategies for Mancala variants 探索和分析曼卡拉变体的战略演变

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633628

Colin Divilly, C. O'Riordan, Seamus Hill

引用次数: 5

Creativity and competitiveness in polyomino-developing game playing agents 多元发展博弈代理的创造力和竞争力

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633647

D. Ashlock, J. Gilbert

引用次数: 1

UCT for PCG PCG的UCT

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633650

C. Browne

引用次数: 5

Soft computing for content generation: Trading market in a basketball management video game 内容生成的软计算:篮球管理视频游戏中的交易市场

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633620

J. Sánchez, Ernestina Menasalvas Ruiz, S. Muelas, A. Latorre, Luis Peña, Sascha Ossowski

引用次数: 2

Online and offline learning in multi-objective Monte Carlo Tree Search 多目标蒙特卡罗树搜索中的在线和离线学习

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633621

Diego Perez Liebana, Spyridon Samothrakis, S. Lucas

引用次数: 11