2016 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第6页

Evolving missions for Dwarf quest dungeons 矮人地下城的进化任务

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860391

Daniel Karavolos, Antonios Liapis, Georgios N. Yannakakis

引用次数: 3

Comparison of rapid action value estimation variants for general game playing 一般游戏中快速动作值估计变量的比较

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860429

C. F. Sironi, M. Winands

引用次数: 14

Human gesture classification by brute-force machine learning for exergaming in physiotherapy 基于暴力机器学习的人体手势分类在物理治疗中的应用

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860414

Francis Deboeverie, Sanne Roegiers, Gianni Allebosch, P. Veelaert, W. Philips

{"title":"Human gesture classification by brute-force machine learning for exergaming in physiotherapy","authors":"Francis Deboeverie, Sanne Roegiers, Gianni Allebosch, P. Veelaert, W. Philips","doi":"10.1109/CIG.2016.7860414","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860414","url":null,"abstract":"In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"92 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73322946","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

Beyond computational intelligence to computational creativity in games 从计算智能到游戏中的计算创造力

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860447

D. Ventura

引用次数: 1

Informed Monte Carlo Tree Search for Real-Time Strategy games 即时策略游戏的通知蒙特卡洛树搜索

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860394

Santiago Ontañón

引用次数: 24

Improved LinUCT and its evaluation on incremental random-feature tree 改进的LinUCT及其对增量随机特征树的评价

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860440

Yusaku Mandai, Tomoyuki Kaneko

{"title":"Improved LinUCT and its evaluation on incremental random-feature tree","authors":"Yusaku Mandai, Tomoyuki Kaneko","doi":"10.1109/CIG.2016.7860440","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860440","url":null,"abstract":"UCT is a standard method of Monte Carlo tree search (MCTS) algorithms, which have been applied to various domains and have achieved remarkable success. This study proposes a family of Leaf-LinUCT, which are improved LinUCT algorithms incorporating LinUCB into MCTS. LinUCB outperforms UCB1 in contextual multi-armed bandit problems, owing to a kind of online learning with ridge regression. However, due to the minimax structure of game trees, ridge regression in LinUCB does not always work well in the context of tree search. In this paper, we remedy the problem and extend our previous work on LinUCT in two ways: (1) by restricting teacher data for regression to the frontier nodes in a current search tree, and (2) by adjusting the feature vector of each internal node to the weighted mean of the feature vector of the descendant nodes. We also present a new synthetic model, incremental-random-feature tree, by extending the standard incremental random tree model. In our model, each node has a feature vector that represents the characteristics of the corresponding position. The elements of a feature vector in a node are randomly changed from those in its parent node by each move, as the heuristic score of a node is randomly changed by each move in the standard incremental random tree model. The experimental results show that our Leaf-LinUCT outperformed UCT and existing LinUCT algorithms, in the incremental-random-feature treeand a synthetic game studied in [1].","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"507 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86840127","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: 0

Sonancia: A multi-faceted generator for horror Sonancia:恐怖的多面生成器

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860445

P. Lopes, Antonios Liapis, Georgios N. Yannakakis

引用次数: 1

A holistic approach for semantic-based game generation 基于语义的游戏生成的整体方法

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860386

Owen Sacco, Antonios Liapis, Georgios N. Yannakakis

引用次数: 6

Learning opening books in partially observable games: Using random seeds in Phantom Go 在部分可观察的游戏中学习打开书籍:在《幻影Go》中使用随机种子

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-08 DOI: 10.1109/CIG.2016.7860389

T. Cazenave, Jialin Liu, F. Teytaud, O. Teytaud

引用次数: 8

Analyzing the robustness of general video game playing agents 一般视频游戏代理的鲁棒性分析

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-07-02 DOI: 10.1109/CIG.2016.7860430

Diego Perez Liebana, Spyridon Samothrakis, J. Togelius, T. Schaul, S. Lucas

引用次数: 18