2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第2页

Enhanced Unsatisfiable Cores for QBF: Weakening Universal to Existential Quantifiers QBF的增强不满足核:弱化全称到存在量词

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00023

Viktor Schuppan

引用次数: 2

A Robust Pickup and Delivery Problem with Uncertain Travel Time 旅行时间不确定的鲁棒取货问题

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00145

Z. Al-Chami, Bechara Bechara, H. Manier, M. Manier

{"title":"A Robust Pickup and Delivery Problem with Uncertain Travel Time","authors":"Z. Al-Chami, Bechara Bechara, H. Manier, M. Manier","doi":"10.1109/ICTAI.2018.00145","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00145","url":null,"abstract":"This paper discusses a robust variant of the well known Pickup and Delivery Problem (PDP) which we called the Robust Selective PDP (RSPDP). In this problem, a fleet composed of several vehicles with a given capacity should satisfy a set of transportation requests while respecting various constraints in relation with the precedence between suppliers and customers, the capacity of vehicles, the opening and closing times of each site. Because of all these constraints, the honoring of all requests over a given period (for example, one day) is sometimes impossible so the selective aspect consists in choosing the sites to be served. This robust variant is characterized by the presence of different scenarios used to handle the uncertain traveling time for each arc. Subject to all scenarios and constraints, the goal is to find the best solution (with the higher profit) which validates all scenarios. This study introduces the mathematical formulation for this new variant. Then, an exact and a metaheuristic approaches will be used to solve it. Several tests have been done, on new generated instances for the considered problem, in order to show the efficiency of developed methods.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114798231","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

Evolving Ensembles of Traffic Lights Controllers 不断发展的交通灯控制器组合

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00148

M. Pilát

引用次数: 1

Probabilistic Argumentation Frameworks with MetaProbLog and ConArg 基于MetaProbLog和ConArg的概率论证框架

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00107

Stefano Bistarelli, Theofrastos Mantadelis, Francesco Santini, Carlo Taticchi

引用次数: 12

Propagation of Idle Times Costs for Fixed Job Scheduling 固定作业调度的空闲时间传播成本

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00113

Wang Ruixin, B. Nicolas

引用次数: 1

Fuzzy Leaky Bucket with Application to Coordinating Smart Appliances in Smart Homes 模糊漏桶在智能家居智能家电协调中的应用

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00137

M. Alamaniotis, Iosif Papadakis Ktistakis

引用次数: 8

Forest Species Recognition Based on Ensembles of Classifiers 基于分类器集合的森林物种识别

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00065

J. Martins, Luiz Oliveira, R. Sabourin, A. Britto

{"title":"Forest Species Recognition Based on Ensembles of Classifiers","authors":"J. Martins, Luiz Oliveira, R. Sabourin, A. Britto","doi":"10.1109/ICTAI.2018.00065","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00065","url":null,"abstract":"Recognition of forest species is a very challenging task thanks to the great intra-class variability. To cope with such a variability, we propose a multiple classifier system based on a two-level classification strategy and microscopic images. By using a divide-and-conquer approach, an image is first divided into several sub-images which are classified independently by each classifier. In a first fusion level, partial decisions for the sub-images are combined to generate a new partial decision for the original image. Then, the second fusion level combines all these new partial decisions to produce the final classification of the original image. To generate the pool of diverse classifiers, we used classical texture-based features as well as keypoint-based features. A series of experiments shows that the proposed strategy achieves compelling results. Compared to the best single classifier, a Support Vector Machine (SVM) trained with a keypoint based feature set, the divide-and-conquer strategy improves the recognition rate in about 4 and 6 percentage points in the first and second fusion levels, respectively. The best recognition rate achieved by this proposed method is 98.47%.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131605053","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: 2

Optimization of Control Agents Shifts in Public Transportation: Tackling Fare Evasion with Machine-Learning 公共交通控制主体转移的优化:用机器学习解决逃票问题

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00070

Jean-Baptiste Delfau, Daphné Pertsekos, M. Chouiten

引用次数: 4

Corpus-Based Augmented Media Posts with Density-Based Clustering for Community Detection 基于语料库的增强媒体帖子与基于密度的聚类社区检测

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00066

W. Mohotti, R. Nayak

{"title":"Corpus-Based Augmented Media Posts with Density-Based Clustering for Community Detection","authors":"W. Mohotti, R. Nayak","doi":"10.1109/ICTAI.2018.00066","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00066","url":null,"abstract":"This paper proposes a corpus-based media posts expansion technique with a density-based clustering method for community detection. To enrich the user content information, firstly all (short-text) media posts of a user are combined with hash tags and URLs available with the posts. The expanded content view is further augmented by the virtual words inferred using the novel concept of matrix factorization based topic proportion vector approximation. This expansion technique deals with the extreme sparseness of short text data which otherwise leads to insufficient word co-occurrence and, in hence, inaccurate outcome. We then propose to group these augmented posts which represent users by identifying the density patches and form user communities. The remaining isolated users are then assigned to communities to which they are found most similar using a distance measure. Experimental results using several Twitter datasets show that the proposed approach is able to deal with common issues attached with (short-text) media posts to form meaningful communities and attain high accuracy compared to relevant benchmarking methods.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133801625","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: 14

LPMLNModels: A Parallel Solver for LPMLN LPMLNModels: LPMLN的并行求解器

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00124

Wei Wu, Hongxiang Xu, Shutao Zhang, Jiaqi Duan, Bin Wang, Zhizheng Zhang, ChengLong He, Shiqiang Zong

{"title":"LPMLNModels: A Parallel Solver for LPMLN","authors":"Wei Wu, Hongxiang Xu, Shutao Zhang, Jiaqi Duan, Bin Wang, Zhizheng Zhang, ChengLong He, Shiqiang Zong","doi":"10.1109/ICTAI.2018.00124","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00124","url":null,"abstract":"LPMLN extends the language of Answer Set Programming (ASP) by assigning a weight degree to each rule so that its stable models do not have to satisfy all LPMLN rules, which is rooted in the manner of Markov Logic Networks (MLN) to handle the uncertainties and inconsistencies in knowledge representation and reasoning. Due to its expressibility, LPMLN has been employed in several real world applications. However, an LPMLN program is much harder to solve than its unweighted counterpart (an ASP program), and only some preliminary solvers have been implemented so far, which is preventing further studies in both theoretical and practical sides. There are three main contributions in this paper. Firstly, we present an LPMLN solver: LPMLNModels, which is able to run concurrently. Secondly, we present parallel methods in LPMLNModels. For splitting set method, we present an algorithm to generate a proper splitting set. For augmented subset method, we present a heuristic method to improve the generation of augmented subsets. Finally, we present hybrid methods in LPMLNModels to better utilize the parallel methods. Experimental results show that our algorithms and improvements in this paper works and hybrid methods have better performance in general.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133829842","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: 2