2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献

Heuristic algorithm for target coverage with connectivity fault-tolerance problem in wireless sensor networks 无线传感器网络中带连通性容错的目标覆盖启发式算法

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880178

N. Hanh, Phan Thi Hong Hanh, Huynh Thi Thanh Binh, N. D. Nghia

{"title":"Heuristic algorithm for target coverage with connectivity fault-tolerance problem in wireless sensor networks","authors":"N. Hanh, Phan Thi Hong Hanh, Huynh Thi Thanh Binh, N. D. Nghia","doi":"10.1109/TAAI.2016.7880178","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7880178","url":null,"abstract":"Wireless sensor networks include a set of sensors being network nodes and a base station. The objective of such systems is to monitor changes which might occur at targets' location. Consequently, assuring the network connectivity is compulsory to maintain a continuous monitoring of given set of targets. However, sensor nodes sometimes stop functioning due to being physically damaged or running out of energy (battery), thus affecting to the connectivity of the system. This paper considers the target coverage with connectivity fault-tolerance problem in wireless sensor networks. The goal of this problem is to come up with a network topology consisting of the least number of sensor nodes that not only offers the greatest target coverage but also maintains the network connectivity even when one random node is defected. This is an NP-Complete combinatorial optimization problem. One heuristic algorithm is proposed to solve the target coverage with connectivity fault-tolerance problem and it is tested on 15 randomly generated instances. Experimental results illustrate a good performance achieved in terms of target coverage, connectivity and fault-tolerance.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115967729","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: 6

Systematic selection of N-tuple networks with consideration of interinfluence for game 2048 博弈2048中考虑相互影响的n元网络的系统选择

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880154

Kiminori Matsuzaki

{"title":"Systematic selection of N-tuple networks with consideration of interinfluence for game 2048","authors":"Kiminori Matsuzaki","doi":"10.1109/TAAI.2016.7880154","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7880154","url":null,"abstract":"The puzzle game 2048, a single-player stochastic game played on a 4 × 4 grid, is the most popular among similar slide-and-merge games. One of the strongest computer players for 2048 uses temporal difference learning (TD learning) on so called N-tuple networks, where the shapes of the N-tuples are given by human based on characteristics of the game. In our previous work (Oka and Matsuzaki, 2016), the authors proposed a systematic method of selecting N-tuples under an assumption that the interinfluence among those N-tuple networksn are negligible. Though the selected N-tuple networks worked fine, there were large gaps between those N-tuple networks and the human-designed networks. In this paper, another systematic and game-characteristics-free method of selecting N-tuples is proposed for game 2048, in which the interinfluence among those N-tuple networks is captured. The proposed method is effective and generic: the selected N-tuple networks are as good as human-designed ones under the same setting, and we can obtain larger (or smaller) N-tuple networks in the same manner. We also report the experiment results when we combine the N-tuple networks and expectimax search.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120986862","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

Ant colony optimization with multi-pheromones for solving constraint satisfaction problems 求解约束满足问题的多信息素蚁群算法

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880183

Takuya Masukane, Kazunori Mizuno

引用次数: 2

A game informatical analysis of RoShamBo 《RoShamBo》的游戏信息分析

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880158

Chetprayoon Panumate, H. Iida, J. Terrillon

引用次数: 3

Using the traffic heterogeneity of Chinese toll highway networks for hierarchical clustering 基于中国收费公路网交通异质性的分层聚类研究

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880174

Shi Fang, Kaigui Bian, Haikun Hong, Kunqing Xie, Yuwen Fu

引用次数: 1

Extraction of proper names from myanmar text using latent dirichlet allocation 使用潜在狄利克雷分配从缅甸文本中提取专有名称

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880176

Yuzana Win, Tomonari Masada

{"title":"Extraction of proper names from myanmar text using latent dirichlet allocation","authors":"Yuzana Win, Tomonari Masada","doi":"10.1109/TAAI.2016.7880176","DOIUrl":"https://doi.org/10.1109/TAAI.2016.7880176","url":null,"abstract":"This paper proposes a method for proper names extraction from Myanmar text by using latent Dirichlet allocation (LDA). Our method aims to extract proper names that provide important information on the contents of Myanmar text. Our method consists of two steps. In the first step, we extract topic words from Myanmar news articles by using LDA. In the second step, we make a post-processing, because the resulting topic words contain some noisy words. Our post-processing, first of all, eliminates the topic words whose prefixes are Myanmar digits and suffixes are noun and verb particles. We then remove the duplicate words and discard the topic words that are contained in the existing dictionary. Consequently, we obtain the words as candidate of proper names, namely personal names, geographical names, unique object names, organization names, single event names, and so on. The evaluation is performed both from the subjective and quantitative perspectives. From the subjective perspective, we compare the accuracy of proper names extracted by our method with those extracted by latent semantic indexing (LSI) and rule-based method. It is shown that both LS] and our method can improve the accuracy of those obtained by rule-based method. However, our method can provide more interesting proper names than LSI. From the quantitative perspective, we use the extracted proper names as additional features in K-means clustering. The experimental results show that the document clusters given by our method are better than those given by LSI and rule-based method in precision, recall and F-score.","PeriodicalId":159858,"journal":{"name":"2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115421360","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

Sentiment analysis on Chinese movie review with distributed keyword vector representation 基于分布式关键词向量表示的中文影评情感分析

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880169

Chun-Han Chu, Chen-Ann Wang, Yung-Chun Chang, Ying-Wei Wu, Yu-Lun Hsieh, W. Hsu

引用次数: 15

Topic suggestion by Bayesian network enhanced tag inference in community question answering 基于贝叶斯网络的话题建议增强了社区问答中的标签推理

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880110

Gerel Tumenbayar, Hung-Yu kao

引用次数: 2

Sensitive information for privacy on social networks a case study of PTT-and-FB linkage 社交网络中的隐私敏感信息——以ptt与fb联动为例

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880181

R. Wang, Rui Wang, Chih-Hua Tai, De-Nian Yang

引用次数: 0

Automatic opinion leader recognition in group discussions 在小组讨论中自动识别意见领袖

2016 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2016-11-01 DOI: 10.1109/TAAI.2016.7880177

Yujeung Ho, Hao-Min Liu, Hui-Hsin Hsu, Chun-Han Lin, Yao-Hua Ho, Ling-Jyh Chen

引用次数: 5