2013 IEEE Conference on Computational Inteligence in Games (CIG)最新文献_第3页

Reactive strategy choice in StarCraft by means of Fuzzy Control 基于模糊控制的《星际争霸》反应性策略选择

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633627

M. Preuss, Daniel Kozakowski, Johan Hagelbäck, H. Trautmann

引用次数: 7

Adaptive game level creation through rank-based interactive evolution 通过基于等级的互动进化创造自适应游戏关卡

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633651

Antonios Liapis, H. P. Martínez, J. Togelius, Georgios N. Yannakakis

引用次数: 30

Using plan-based reward shaping to learn strategies in StarCraft: Broodwar 在《星际争霸:母巢之战》中使用基于计划的奖励塑造来学习策略

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633622

Kyriakos Efthymiadis, D. Kudenko

{"title":"Using plan-based reward shaping to learn strategies in StarCraft: Broodwar","authors":"Kyriakos Efthymiadis, D. Kudenko","doi":"10.1109/CIG.2013.6633622","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633622","url":null,"abstract":"StarCraft: Broodwar (SC:BW) is a very popular commercial real strategy game (RTS) which has been extensively used in AI research. Despite being a popular test-bed reinforcement learning (RL) has not been evaluated extensively. A successful attempt was made to show the use of RL in a small-scale combat scenario involving an overpowered agent battling against multiple enemy units [1]. However, the chosen scenario was very small and not representative of the complexity of the game in its entirety. In order to build an RL agent that can manage the complexity of the full game, more efficient approaches must be used to tackle the state-space explosion. In this paper, we demonstrate how plan-based reward shaping can help an agent scale up to larger, more complex scenarios and significantly speed up the learning process as well as how high level planning can be combined with learning focusing on learning the Starcraft strategy, Battlecruiser Rush. We empirically show that the agent with plan-based reward shaping is significantly better both in terms of the learnt policy, as well as convergence speed when compared to baseline approaches which fail at reaching a good enough policy within a practical amount of time.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"15 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":"125028997","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: 24

Creating large numbers of game AIs by learning behavior for cooperating units 通过学习合作单位的行为创造大量的游戏ai

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633608

Stephen Wiens, J. Denzinger, Sanjeev Paskaradevan

引用次数: 5

LGOAP: Adaptive layered planning for real-time videogames LGOAP:实时电子游戏的自适应分层规划

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633624

G. Maggiore, Carlos Santos, D. Dini, Frank Peters, Ha Bouwknegt, P. Spronck

引用次数: 3

Automatic generation and analysis of physics-based puzzle games 自动生成和分析基于物理的益智游戏

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633633

M. Shaker, Mhd Hasan Sarhan, Ola Al Naameh, Noor Shaker, J. Togelius

引用次数: 49

Analytics-driven dynamic game adaption for player retention in Scrabble 《Scrabble》中玩家留存率的分析驱动动态游戏调整

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633632

Brent E. Harrison, D. Roberts

{"title":"Analytics-driven dynamic game adaption for player retention in Scrabble","authors":"Brent E. Harrison, D. Roberts","doi":"10.1109/CIG.2013.6633632","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633632","url":null,"abstract":"This paper shows how game analytics can be used in conjunction with an adaptive system in order to increase player retention at the level of individual game sessions in Scrabblesque, a Flash game based on the popular board game Scrabble. In this paper, we use game analytic knowledge to create a simplified search space (called the game analytic space) of board states. We then target a distribution of game analytic states that are predictive of players playing a complete game session of Scrabblesque in order to increase player retention. Our adaptive system then has a computer-controlled AI opponent take moves that will help realize this distribution of game analytic states with the ultimate goal of reducing the quitting rate. We test this system by performing a user study in which we compare how many people quit playing the adaptive version of Scrabblesque early and how many people quit playing a nonadaptive version of Scrabblesque early. We also compare how well the adaptive version of Scrabblesque was able to influence player behavior as described by game analytics. Our results show that our adaptive system is able to produce a significant reduction in the quitting rate (p = 0.03) when compared to the non-adaptive version. In addition, the adaptive version of Scrabblesque is able to better fit a target distribution of game analytic states when compared to the non-adaptive version.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"49 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":"125782267","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: 19

Give me a reason to dig Minecraft and psychology of motivation 给我一个挖掘《我的世界》和动机心理学的理由

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633612

Alessandro Canossa, Josep B. Martinez, J. Togelius

引用次数: 45

Portfolio greedy search and simulation for large-scale combat in starcraft 星际争霸中大规模战斗的组合贪婪搜索与模拟

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633643

David Churchill, M. Buro

{"title":"Portfolio greedy search and simulation for large-scale combat in starcraft","authors":"David Churchill, M. Buro","doi":"10.1109/CIG.2013.6633643","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633643","url":null,"abstract":"Real-time strategy video games have proven to be a very challenging area for applications of artificial intelligence research. With their vast state and action spaces and real-time constraints, existing AI solutions have been shown to be too slow, or only able to be applied to small problem sets, while human players still dominate RTS AI systems. This paper makes three contributions to advancing the state of AI for popular commercial RTS game combat, which can consist of battles of dozens of units. First, we present an efficient system for modelling abstract RTS combat called SparCraft, which can perform millions of unit actions per second and visualize them. We then present a modification of the UCT algorithm capable of performing search in games with simultaneous and durative actions. Finally, a novel greedy search algorithm called Portfolio Greedy Search is presented which uses hill climbing and accurate playout-based evaluations to efficiently search even the largest combat scenarios. We demonstrate that Portfolio Greedy Search outperforms state of the art Alpha-Beta and UCT search methods for large StarCraft combat scenarios of up to 50 vs. 50 units under real-time search constraints of 40 ms per search episode.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"1 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":"134172644","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: 124

Spatial game analytics and visualization 空间游戏分析和可视化

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633629

Anders Drachen, Matthias Schubert

{"title":"Spatial game analytics and visualization","authors":"Anders Drachen, Matthias Schubert","doi":"10.1109/CIG.2013.6633629","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633629","url":null,"abstract":"The recently emerged field of game analytics and the development and adaptation of business intelligence techniques to support game design and development has given data-driven techniques a direct role in game development. Given that all digital games contain some sort of spatial operation, techniques for spatial analysis had their share in these developments. However, the methods for analyzing and visualizing spatial and spatio-temporal patterns in player behavior being used by the game industry are not as diverse as the range of techniques utilized in game research, leaving room for a continuing development. This paper presents a review of current work on spatial and spatio-temporal game analytics across industry and research, describing and defining the key terminology, outlining current techniques and their application. We summarize the current problems and challenges in the field, and present four key areas of spatial and spatio-temporal analytics: Spatial Outlier Detection, Spatial Clustering, Spatial Predictive Models, Spatial Pattern and Rule Mining. All key areas are well-established outside the context of games and hold the potential to reshape the research roadmap in game analytics.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"31 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":"121501860","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: 25