2015 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第7页

Mining game logs to create a playbook for unit AIs 挖掘游戏日志为单位ai创建剧本

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317897

Daniel Wehr, J. Denzinger

引用次数: 4

On imitating Connect-4 game trajectories using an approximate n-tuple evaluation function 用近似n元组评价函数模拟Connect-4游戏轨迹

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317961

T. Runarsson, S. Lucas

引用次数: 1

Toward avatar models to enhance performance and engagement in educational games 利用虚拟角色模型来提高教育类游戏的表现和参与度

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317959

Dominic Kao, D. Harrell

{"title":"Toward avatar models to enhance performance and engagement in educational games","authors":"Dominic Kao, D. Harrell","doi":"10.1109/CIG.2015.7317959","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317959","url":null,"abstract":"This paper presents work toward better understanding the roles that avatars can play in supporting learning in educational games. Specifically, the paper presents results of empirical studies on the impact of avatar type on learner/player performance and engagement. These results constitute work establishing baseline understandings to inform our longer term goal of developing models that use dynamic avatars to best support learners in educational games. Our aim is motivated by a convergence of research in the social sciences establishing that identity plays an important role in learning. Of note, aspects of social identity (e.g., race, ethnicity, and gender) have been shown to impact student performance [1] via triggering stereotypes [2]. Recently, performance and engagement studies in our educational game for Science, Technology, Engineering and Mathematics (STEM) learning suggest these same phenomena can be activated through virtual avatars [3], [4]. Here, we present results of a comparative study between avatars in the likeness of players and avatars as geometric shapes. In our STEM learning game, results show that players that had selected and used a shape avatar had significantly higher performance than players that had customized and used a likeness avatar. Players using the shape avatar also had significantly higher self-reported engagement, despite having lower self-reported affect towards the avatar.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114634359","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: 20

Enhancements in Monte Carlo tree search algorithms for biased game trees 改进的蒙特卡罗树搜索算法对有偏差的游戏树

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317924

Takahisa Imagawa, Tomoyuki Kaneko

{"title":"Enhancements in Monte Carlo tree search algorithms for biased game trees","authors":"Takahisa Imagawa, Tomoyuki Kaneko","doi":"10.1109/CIG.2015.7317924","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317924","url":null,"abstract":"Monte Carlo tree search (MCTS) algorithms have been applied to various domains and achieved remarkable success. However, it is relatively unclear what game properties enhance or degrade the performance of MCTS, while the largeness of search space including pruning efficiency mainly governs the performance of classical minimax search, assuming a decent evaluation function is given. Existing research has shown that the distribution of suboptimal moves and the non-uniformity of tree shape are more important than the largeness of state space in discussing the performance of MCTS. Our study showed that another property, bias in suboptimal moves, is also important, and we present an enhancement to better handle such situations. We focus on a game tree in which the game-theoretical value is even, while suboptimal moves for a player tend to contain more inferior moves than those for the opponent. We conducted experiments on a standard incremental tree model with various MCTS algorithms based on UCB1, KL-UCB, or Thompson sampling. The results showed that the bias in suboptimal moves degraded the performance of all algorithms and that our enhancement alleviated the effect caused by this property.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116236745","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

A platform for turn-based strategy games, with a comparison of Monte-Carlo algorithms 一个回合制策略游戏平台，与蒙特卡洛算法比较

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317962

Tsubasa Fujiki, Kokolo Ikeda, Simon Viennot

引用次数: 5

Understanding players' identities and behavioral archetypes from avatar customization data 从角色自定义数据中理解玩家的身份和行为原型

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317944

Chong-U Lim, D. Harrell

{"title":"Understanding players' identities and behavioral archetypes from avatar customization data","authors":"Chong-U Lim, D. Harrell","doi":"10.1109/CIG.2015.7317944","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317944","url":null,"abstract":"Virtual identities are an integral part of peoples' lives, from online shopping accounts to social networking profiles, from intelligent tutors to videogame avatars. In many videogames, players construct avatars to represent themselves within virtual environments and research has shown that players' sociocultural identities influence their avatar construction and can be a proxy for inferring their values in the non-virtual (real) world. In this paper, we present a computational approach to modeling players' real-world identities using behavioral data collected during the avatar customization process. We used archetypal analysis on player interaction data to develop “behavioral archetypes”, which are models of prototypical behavior patterns exhibited by players during the avatar customization process. We modeled patterns of (1) “avatar gender-preferring” behaviors (preferences for a particular avatar gender), (2) “styler” behaviors (preferences for different parts of their avatars, e.g., hair-styler, head-styler, etc.,) and (3) preferences for using avatars of a different gender (“gender-bending”) or the same gender (“gender-synchronizing”) as the players'. In a user-study with 190 participants, the behavioral archetype model trained via supervised learning had high accuracy (81%) in classifying players' real-world gender using only behavioral data. We show that behavioral archetypes are effective for understanding players in terms of their customization behaviors, real-world genders, and virtual avatar genders.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120954340","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: 8

Neuroevolution for General Video Game Playing 一般电子游戏的神经进化

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317943

Spyridon Samothrakis, Diego Perez Liebana, S. Lucas, M. Fasli

引用次数: 15

Automated puzzle difficulty estimation 自动解谜难度估计

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317913

M. V. Kreveld, M. Löffler, P. Mutser

引用次数: 13

How costly is a good compromise: Multi-objective TORCS controller parameter optimization 多目标TORCS控制器参数优化的代价有多大

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317933

Jan Quadflieg, G. Rudolph, M. Preuss

{"title":"How costly is a good compromise: Multi-objective TORCS controller parameter optimization","authors":"Jan Quadflieg, G. Rudolph, M. Preuss","doi":"10.1109/CIG.2015.7317933","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317933","url":null,"abstract":"We extend existing work on the offline parameter optimization for The Open Racing Car Simulator (TORCS) controllers and take it to a truly multi-objective level. By means of the (100+1)-SMS-EMOA, we optimize the parameter set for the controller named `Mr. Racer' on three significantly different tracks simultaneously, with a budget of 3 × 6000 function evaluations. In the ten runs performed, the SMS-EMOA reliably finds good compromise solutions, as well as selfish optima that are comparable in quality to the ones previously obtained by means of the CMA-ES for each particular track. We further analyze how to select parameter set(s) for the controller from the results of the evolutionary optimization, for the case that a controller has the chance to further finetune its behavior on an unknown track, as it is done in the warinup phase of the Simulated Car Racing Championship. Experimental results show that one parameter set is not sufficient. To perform well, a controller as Mr. Racer needs at least two different parameter sets from which it can choose in the warinup stage. The best performance is gained by using three parameter sets, which leads to an increase in championship points of 17% compared to the 2013 version of Mr. Racer.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125613532","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

Belief-state Monte-Carlo tree search for Phantom games 幻影游戏的信念状态蒙特卡洛树搜索

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-08-01 DOI: 10.1109/CIG.2015.7317917

Jiao Wang, Tan Zhu, Hongye Li, Chu-Hsuan Hsueh, I-Chen Wu

{"title":"Belief-state Monte-Carlo tree search for Phantom games","authors":"Jiao Wang, Tan Zhu, Hongye Li, Chu-Hsuan Hsueh, I-Chen Wu","doi":"10.1109/CIG.2015.7317917","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317917","url":null,"abstract":"Playing games with imperfect information is a very challenging issue in AI field due to its high complexity. Phantom game is a kind of such games, which usually has a large game-tree complexity and has little research achievements until now. In Phantom games, rational human players commonly select actions according to their beliefs in the game, which can be represented as a concept of belie f-state. To the best of our knowledge, our paper is the first article to incorporate belief-states in the Monte-Carlo Tree Search, and the proposed algorithm is named BS-MCTS (Belief-state Monte-Carlo Tree Search). In BS-MCTS, a belief-state tree, in which each node is a belief-state, is constructed and the search procedure is in accordance with beliefs updated by heuristic search information. We also present two novel implementations in the belief learning, that are Opponent Guessing and Opponent Predicting, concerning the probability on the possible states and on future actions of the opponent respectively. To prove the effectiveness of our algorithm, BS-MCTS is applied to Phantom Tic-Tac-Toe and Phantom Go against other Monte-Carlo methods. The experimental results demonstrate that our method is outstanding and advanced. Moreover, based on BS-MCTS, our Phantom Go program had consecutively won three championships in Chinese National Tournaments.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124555165","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