2014 IEEE 26th International Conference on Tools with Artificial Intelligence最新文献_第10页

An Approach to Estimate Traffic Speed Based on Cellular Network Signaling Data on Highways 基于蜂窝网络信令数据的高速公路交通速度估计方法

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.139

Zhixin Song, T. Zhu, Dongdong Wu, Suai Lius

引用次数: 1

A Framework to Support the Selection of Software Technologies by Search-Based Strategy 支持基于搜索策略的软件技术选择的框架

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.148

Aurélio da Silva Grande, R. Rodrigues, A. C. Dias-Neto

引用次数: 2

Microdebates App for Android: A Tool for Participating in Argumentative Online Debates Using a Handheld Device Android微辩论应用:一个使用手持设备参与在线辩论的工具

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.122

Nefise Yaglikci, Paolo Torroni

引用次数: 7

Knowledge Complement for Monte Carlo Tree Search: An Application to Combinatorial Games 蒙特卡罗树搜索的知识补充:在组合博弈中的应用

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.151

André Fabbri, Frederic Armetta, Éric Duchêne, S. Hassas

{"title":"Knowledge Complement for Monte Carlo Tree Search: An Application to Combinatorial Games","authors":"André Fabbri, Frederic Armetta, Éric Duchêne, S. Hassas","doi":"10.1109/ICTAI.2014.151","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.151","url":null,"abstract":"MCTS (Monte Carlo Tree Search) is a well-known and efficient process to cover and evaluate a large range of states for combinatorial problems. We choose to study MCTS for the Computer Go problem, which is one of the most challenging problem in the field in Artificial Intelligence. For this game, a single combinatorial approach does not always lead to a reliable evaluation of the game states. In order to enhance MCTS ability to tackle such problems, one can benefit from game specific knowledge in order to increase the accuracy of the game state evaluation. Such a knowledge is not easy to acquire. It is the result of a constructivist learning mechanism based on the experience of the player. That is why we explore the idea to endow the MCTS with a process inspired by constructivist learning, to self-acquire knowledge from playing experience. In this paper, we propose a complementary process for MCTS called BHRF (Background History Reply Forest), which allows to memorize efficient patterns in order to promote their use through the MCTS process. Our experimental results lead to promising results and underline how self-acquired data can be useful for MCTS based algorithms.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122390226","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: 3

Planning and Acting with Temporal and Hierarchical Decomposition Models 计划和行动与时间和层次分解模型

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.27

Filip Dvorak, R. Barták, Arthur Bit-Monnot, F. Ingrand, M. Ghallab

{"title":"Planning and Acting with Temporal and Hierarchical Decomposition Models","authors":"Filip Dvorak, R. Barták, Arthur Bit-Monnot, F. Ingrand, M. Ghallab","doi":"10.1109/ICTAI.2014.27","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.27","url":null,"abstract":"This paper reports on FAPE (Flexible Acting and Planning Environment), a framework integrating acting and planning on the basis of the ANML modeling language. ANML is a recent proposal motivated by combining the expressiveness of the timeline representation with decomposition methods of Hierarchical Task Networks (HTN). Our current focus is not efficient temporal planning per se, but the tight integration of acting and planning. This integration is addressed by: (i) extending HTN methods with the refinement of planned actions with skills, expressed in PRS, to map actions into low-level commands, (ii) interleaving the planning process with acting, the former performs plan repair and replanning, while the latter implements the skill-based refinements, and (iii) executing commands with a dispatching mechanism that synchronizes observed time points of action effects and events with planned time. FAPE has been integrated to a PR2 robot and experimented in a home-like environment. The paper presents how planning is performed and integrated with acting and describes briefly the robotics experiments.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"295 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121330987","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: 46

Argumentation-Based Collaborative Decisions for Design 基于论证的设计协同决策

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.124

Valentinos Evripidou, Lucas Carstens, Francesca Toni, David Cabanillas

引用次数: 2

Using Neural Networks in the Identification of Signatures for Prediction of Alzheimer's Disease 神经网络在阿尔茨海默病预测特征识别中的应用

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.43

L. Dantas, M. Valença

{"title":"Using Neural Networks in the Identification of Signatures for Prediction of Alzheimer's Disease","authors":"L. Dantas, M. Valença","doi":"10.1109/ICTAI.2014.43","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.43","url":null,"abstract":"Alzheimer's disease (AD) is now considered the most common type of dementia in the population. Although it is a degenerative and irreversible disease, if diagnosed early, medications may be administered to slow the progression of symptoms and provide a better quality of life for the patient. Herbert et al. And Gòmez conducted studies with classifiers contained in the software Weka using a database with values of 120 blood proteins, and they noticed that they could classify the patient may or may not be diagnosed with AD with an accuracy rate of 93% and 65%, respectively. Thus, this study aims to use neural networks such as Multi-layer Perceptron, Extreme-learning Machine and Reservoir Computing to perform early diagnosis of a patient with or without AD and Mild Cognitive Impairment (MCI), another common type of disease. This article also envisions to utilize the Random Forest Algorithm and the feature selection method available on Weka called Info Gain Attribute Eval to select proteins from the original set and, thus, create a new protein signature. Through experiments it can be concluded that the best performance was obtained with the MLP and the new signatures created with the Random Forest achieved better results than those available in the literature.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116534078","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: 5

Partition Clustering for GIS Map Data Protection GIS地图数据保护的分区聚类

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.128

A. Abubahia, Ella Haig

引用次数: 8

A Comparative Study of Extreme Learning Machine Pruning Based on Detection of Linear Independence 基于线性独立性检测的极限学习机剪枝方法的比较研究

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.20

L. D. Tavares, R. R. Saldanha, D. Vieira, A. C. Lisboa

引用次数: 3

A Hybrid Sentiment Lexicon for Social Media Mining 一种用于社交媒体挖掘的混合情感词典

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.76

A. Muhammad, N. Wiratunga, Robert Lothian

引用次数: 10