2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第3页

Electricity Theft Detection Using Generative Models 基于生成模型的窃电检测

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00050

Qianru Zhang, Meng Zhang, Tinghuan Chen, Jinan Fan, Zhou Yang, Guoqing Li

引用次数: 11

Possibilistic Networks: MAP Query and Computational Analysis 可能性网络:MAP查询与计算分析

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00142

S. Benferhat, Amélie Levray, Karim Tabia

引用次数: 1

Historical Best Q-Networks for Deep Reinforcement Learning 历史上最好的深度强化学习q网络

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00012

Wenwu Yu, Rui Wang, Ruiying Li, Jing Gao, Xiaohui Hu

引用次数: 5

Dynamic Ensemble Selection by K-Nearest Local Oracles with Discrimination Index 基于判别指数的k -最近局部预言机动态集成选择

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00120

Marcelo Pereira, A. Britto, Luiz Oliveira, R. Sabourin

引用次数: 7

Tracking Branches in Trees - A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems 树的分支跟踪——解决部分有序HTN规划问题的命题编码

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00022

G. Behnke, D. Höller, Susanne Biundo-Stephan

引用次数: 21

Hybrid CODBA-II Algorithm Coupling a Co-Evolutionary Decomposition-Based Algorithm with Local Search Method to Solve Bi-Level Combinatorial Optimization 基于协同进化分解和局部搜索的混合CODBA-II算法求解双层次组合优化问题

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00084

Abir Chaabani, L. B. Said

{"title":"Hybrid CODBA-II Algorithm Coupling a Co-Evolutionary Decomposition-Based Algorithm with Local Search Method to Solve Bi-Level Combinatorial Optimization","authors":"Abir Chaabani, L. B. Said","doi":"10.1109/ICTAI.2018.00084","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00084","url":null,"abstract":"Bi-level optimization problems (BLOPs) are a class of challenging problems with two levels of optimization tasks. The usefulness of bi-level optimization in designing hierarchical decision processes prompted several researchers, in particular the evolutionary computation community, to pay more attention to such kind of problems. Several solution approaches have been proposed to solve these problems; however, most of them are restricted to the continuous case. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-based Algorithm (CODBA-II) to solve combinatorial bi-level problems. CODBA-II scheme has been able to improve the bi-level performance and to bring down the computational expense significantly as compared to other competitive approaches within this research area. In this paper, we present an extension of the recently proposed CODBA-II algorithm. The improved version, called CODBA-IILS, further improves the algorithm by incorporating a local search process to both upper and lower levels in order to help in faster convergence of the algorithm. The improved results have been demonstrated on two different sets of test problems based on the bi-level production-distribution problems in supply chain management, and comparison results against the contemporary approaches are also provided.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"23 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132736916","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

Predicting Stances in Twitter Conversations for Detecting Veracity of Rumors: A Neural Approach 预测Twitter对话中的立场以检测谣言的真实性:一种神经方法

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00021

Lahari Poddar, W. Hsu, M. Lee, Shruti Subramaniyam

{"title":"Predicting Stances in Twitter Conversations for Detecting Veracity of Rumors: A Neural Approach","authors":"Lahari Poddar, W. Hsu, M. Lee, Shruti Subramaniyam","doi":"10.1109/ICTAI.2018.00021","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00021","url":null,"abstract":"Detecting rumors is a crucial task requiring significant time and manual effort in forms of investigative journalism. In social media such as Twitter, unverified information can get disseminated rapidly making early detection of potentially false rumors critical. We observe that the early reactions of people towards an emerging claim can be predictive of its veracity. We propose a novel neural network architecture using the stances of people engaging in a conversation on Twitter about a rumor for detecting its veracity. Our proposed solution comprises two key steps. We first detect the stance of each individual tweet, by considering the textual content of the tweet, its timestamp, as well as the sequential conversation structure leading up to the target tweet. Then we use the predicted stances of all tweets in a conversation tree to determine the veracity of the original rumor. We evaluate our model on the SemEval2017 rumor detection dataset and demonstrate that our solution outperforms the state-of-the-art approaches for both stance prediction and rumor veracity prediction tasks.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"15 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":"134321990","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: 38

Effective Ant Colony Optimization Solution for the Brazilian Family Health Team Scheduling Problem 巴西家庭医疗队调度问题的有效蚁群优化解决方案

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00082

Willian Heitor Martins, Lucia Helena Souza Alves de Santiago, Rafael de Santiago, L. Lamb

引用次数: 0

Information-Oriented Evaluation Metric for Dialogue Response Generation Systems 对话响应生成系统的信息导向评价度量

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00122

Peiqi Liu, S. Zhong, Zhong Ming, Yan Liu

引用次数: 1

Determining Representativeness of Training Plans: A Case of Macro-Operators 培训计划代表性的确定:以宏观算子为例

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00081

L. Chrpa, M. Vallati

{"title":"Determining Representativeness of Training Plans: A Case of Macro-Operators","authors":"L. Chrpa, M. Vallati","doi":"10.1109/ICTAI.2018.00081","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00081","url":null,"abstract":"Most learning for planning approaches rely on analysis of training plans. This is especially the case for one of the best-known learning approach: the generation of macro-operators (macros). These plans, usually generated from a very limited set of training tasks, must provide a ground to extract useful knowledge that can be fruitfully exploited by planning engines. In that, training tasks have to be representative of the larger class of planning tasks on which planning engines will then be run. A pivotal question is how such a set of training tasks can be selected. To address this question, here we introduce a notion of structural similarity of plans. We conjecture that if a class of planning tasks presents structurally similar plans, then a small subset of these tasks is representative enough to learn the same knowledge (macros) as could be learnt from a larger set of tasks of the same class. We have tested our conjecture by focusing on two state-of-the-art macro generation approaches. Our large empirical analysis considering seven state-of-the-art planners, and fourteen benchmark domains from the International Planning Competition, generally confirms our conjecture which can be exploited for selecting small-yet-informative training sets of tasks.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"85 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":"116978428","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