2013 IEEE Conference on Computational Inteligence in Games (CIG)最新文献_第6页

Knowledge discovery for characterizing team success or failure in (A)RTS games 在RTS游戏中描述团队成败的知识发现

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-10-17 DOI: 10.1109/CIG.2013.6633645

Pu Yang, D. Roberts

{"title":"Knowledge discovery for characterizing team success or failure in (A)RTS games","authors":"Pu Yang, D. Roberts","doi":"10.1109/CIG.2013.6633645","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633645","url":null,"abstract":"When doing post-competition analysis in team games, it can be hard to figure out if a team members' character attribute development has been successful directly from game logs. Additionally, it can also be hard to figure out how the performance of one team member affects the performance of another. In this paper, we present a data-driven method for automatically discovering patterns in successful team members' character attribute development in team games. We first represent team members' character attribute development using time series of informative attributes. We then find the thresholds to separate fast and slow attribute growth rates using clustering and linear regression. We create a set of categorical attribute growth rates by comparing against the thresholds. If the growth rate is greater than the threshold it is categorized as fast growth rate; if the growth rate is less than the threshold it is categorized as slow growth rate. After obtaining the set of categorical attribute growth rates, we build a decision tree on the set. Finally, we characterize the patterns of team success in terms of rules which describe team members' character attribute growth rates. We present an evaluation of our methodology on three real games: DotA,1 Warcraft III,2 and Starcraft II.3 A standard machine-learning-style evaluation of the experimental results shows the discovered patterns are highly related to successful team strategies and achieve an average 86% prediction accuracy when testing on new game logs.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"39 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":"124706437","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: 9

Examination of graphs in Multiple Agent Genetic Networks for Iterated Prisoner's Dilemma 迭代囚徒困境的多智能体遗传网络图的检验

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633635

J. A. Brown

引用次数: 4

Finding robust strategies to defeat specific opponents using case-injected coevolution 寻找强大的策略，以击败特定的对手使用案例注入的共同进化

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633656

Christopher A. Ballinger, S. Louis

引用次数: 5

Monte Carlo Tree Search with macro-actions and heuristic route planning for the Multiobjective Physical Travelling Salesman Problem 多目标物理旅行商问题的宏行为蒙特卡罗树搜索和启发式路径规划

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633658

E. Powley, D. Whitehouse, P. Cowling

{"title":"Monte Carlo Tree Search with macro-actions and heuristic route planning for the Multiobjective Physical Travelling Salesman Problem","authors":"E. Powley, D. Whitehouse, P. Cowling","doi":"10.1109/CIG.2013.6633658","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633658","url":null,"abstract":"This paper describes our entry to the Multiobjective Physical Travelling Salesman Problem (MO-PTSP) competition at the IEEE CIG 2013 conference. MO-PTSP combines the classical Travelling Salesman Problem with the task of steering a simulated spaceship on the 2-D plane, requiring that the controller minimises the three objectives of time taken, fuel consumed and damage incurred. Our entry to the MO-PTSP competition builds upon our winning entry to the previous (single-objective) PTSP competitions. This controller consists of two key components: a pre-planning stage using a classical TSP solver with a path cost measure that takes the physics of the problem into account, and a steering controller using Monte Carlo Tree Search (MCTS) with macro-actions (repeated actions), depth limiting and a heuristic fitness function for nonterminal states. We demonstrate that by modifying the two fitness functions we can produce effective behaviour in MO-PTSP without the need for major modifications to the overall architecture. The fitness functions used by our controller have several parameters, which must be set to ensure the best performance. Given the number of parameters and the difficulty of optimising a controller to satisfy multiple objectives in a search space which is many orders of magnitude larger than that encountered in a turn-based game such as Go, we show that informed hand tuning of parameters is insufficient for this task. We present an automatic parameter tuning method using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, which produced parameter settings that dominate our hand tuned parameters. Additionally we show that the robustness of the controller using hand tuned parameters can be improved by detecting when the controller is trapped in a poor quality local optimum and escaping by switching to an alternate fitness function.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125754218","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: 18

MirrorBot: Using human-inspired mirroring behavior to pass a turing test MirrorBot:利用人类的镜像行为来通过图灵测试

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633618

Mihai Polceanu

引用次数: 23

Measuring interestingness of continuous game problems 测量连续游戏问题的趣味性

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633641

S. A. Roberts, S. Lucas

引用次数: 4

Predicting skill from gameplay input to a first-person shooter 从第一人称射击游戏的玩法输入中预测技能

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633655

David Buckley, Ke Chen, Joshua D. Knowles

引用次数: 18

Modeling player preferences in avatar customization using social network data: A case-study using virtual items in Team Fortress 2 使用社交网络数据在角色定制中建模玩家偏好:《军团要塞2》中虚拟道具的案例研究

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633636

Chong-U Lim, D. Harrell

{"title":"Modeling player preferences in avatar customization using social network data: A case-study using virtual items in Team Fortress 2","authors":"Chong-U Lim, D. Harrell","doi":"10.1109/CIG.2013.6633636","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633636","url":null,"abstract":"Game players express their values related to self-expression through various means such as avatar customization, gameplay styles, and interactions with other players. Multiplayer online games, now often integrated with social networks, provide social contexts in which player-to-player interactions take place, for example, through the trading of virtual items between players. Building upon a theoretical framework based in computer science and cognitive science, we present results from a novel approach to modeling and analyzing player values in terms of both preferences made in avatar customization, and patterns in social networking use. Our approach resulted in the development of the Steam-Player-Preference Analyzer (Steam-PPA) system, which (1) performs advanced data collection on publicly available social networking profile information and (2) the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including clustering, natural language processing, and support vector machines (SVM) to perform inference on the data. As an initial case-study, we apply both systems to the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 by analyzing information from player accounts on the social network Steam, together with avatar customization information generated by the player within the game. Our model uses social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of the players' avatar.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133410521","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: 11

Replay-based strategy prediction and build order adaptation for StarCraft AI bots 《星际争霸》AI机器人基于重玩的策略预测和构建顺序适应

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633666

Ho-Chul Cho, Kyung-Joong Kim, Sung-Bae Cho

{"title":"Replay-based strategy prediction and build order adaptation for StarCraft AI bots","authors":"Ho-Chul Cho, Kyung-Joong Kim, Sung-Bae Cho","doi":"10.1109/CIG.2013.6633666","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633666","url":null,"abstract":"StarCraft is a real-time strategy (RTS) game and the choice of strategy has big impact on the final results of the game. For human players, the most important thing in the game is to select the strategy in the early stage of the game. Also, it is important to recognize the opponent's strategy as quickly as possible. Because of the “fog-of-war” in the game, the player should send a scouting unit to opponent's hidden territory and the player predicts the types of strategy from the partially observed information. Usually, expert players are familiar with the relationships between two build orders and they can change the current build order if his choice is not strong to the opponent's strategy. However, players in AI competitions show quite different behaviors compared to the human leagues. For example, they usually have a pre-selected build order and rarely change their order during the game. In fact, the computer players have little interest in recognizing opponent's strategy and scouting units are used in a limited manner. The reason is that the implementation of scouting behavior and the change of build order from the scouting vision is not a trivial problem. In this paper, we propose to use replays to predict the strategy of players and make decision on the change of build orders. Experimental results on the public replay files show that the proposed method predicts opponent's strategy accurately and increases the chance of winning in the game.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122904346","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: 32

Play style: Showing your age 游戏风格:显示你的年龄

2013 IEEE Conference on Computational Inteligence in Games (CIG) Pub Date : 2013-08-01 DOI: 10.1109/CIG.2013.6633616

S. Tekofsky, P. Spronck, A. Plaat, Jaap van den Herik, J. Broersen

{"title":"Play style: Showing your age","authors":"S. Tekofsky, P. Spronck, A. Plaat, Jaap van den Herik, J. Broersen","doi":"10.1109/CIG.2013.6633616","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633616","url":null,"abstract":"Age has been shown to influence our preferences, choices, and cognitive performance. We expect this influence to be visible in the play style of an individual. Player models would then benefit from incorporating age, allowing developers to offer an increasingly personalized game experience to the player. To investigate the relationship between age and play style, we set out to determine how much of the variance in a player's age can be explained by his play style. For this purpose, we used the data from a survey (`PsyOps') among 13,376 `Battlefield 3' players. Starting out with 60 play style variables, we found that 45.7% of the variance in age can be explained by 46 play style variables. Furthermore, similar percentages of variance in age are explained when the sample is divided along gaming platform: 31 play style variables explain 43.1% on PC; 30 play style variables explain 53.9% on Xbox 360; 28 play style variables explain 51.7% on Playstation 3. Our findings have a high external validity due to the large and heterogeneous nature of the sample. The strength of the relationship between age and play style is considered `large' according to Cohen's classification. Previous research indicates that the nature of the relationship between age and play style is likely to be based on life-time developments in cognitive performance, motivation, and personality. All in all, our findings merit a recommendation to incorporate age in future player models.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131512721","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