2016 5th Brazilian Conference on Intelligent Systems (BRACIS)最新文献_第7页

Evaluating Binary Encoding Techniques for WiSARD 评估WiSARD的二进制编码技术

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.029

Andressa Kappaun, Karine Camargo, Fábio Medeiros Rangel, Fabrício Firmino de Faria, P. Lima, Jonice Oliveira

引用次数: 10

Anomaly Detection under Cost Constraint 成本约束下的异常检测

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.053

Bruno Barbarioli, R. Assunção

引用次数: 1

SSDP: A Simple Evolutionary Approach for Top-K Discriminative Patterns in High Dimensional Databases 高维数据库中Top-K判别模式的一种简单进化方法

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.072

Tarcisio Pontes, Renato Vimieiro, Teresa B Ludermir

引用次数: 2

Improved Airport Ground Traffic Control with Domain-Dependent Heuristics 基于域相关启发式的机场地面交通管制改进

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.024

Augusto B. Corrêa, A. Pereira, M. Ritt

引用次数: 1

Multiagent Circumnavigation by Reactive Decentralized Algorithm 基于响应式分散算法的多智能体绕航

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.085

Edson Wilian da Silva, Luiz A. Celiberto, Robson Santos Franca

引用次数: 0

Towards Knowledge Transfer in Deep Reinforcement Learning 深度强化学习中的知识转移

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.027

R. Glatt, Felipe Leno da Silva, Anna Helena Reali Costa

{"title":"Towards Knowledge Transfer in Deep Reinforcement Learning","authors":"R. Glatt, Felipe Leno da Silva, Anna Helena Reali Costa","doi":"10.1109/BRACIS.2016.027","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.027","url":null,"abstract":"Driven by recent developments in the area of Artificial Intelligence research, a promising new technology for building intelligent agents has evolved. The technology is termed Deep Reinforcement Learning (DRL) and combines the classic field of Reinforcement Learning (RL) with the representational power of modern Deep Learning approaches. DRL enables solutions for difficult and high dimensional tasks, such as Atari game playing, for which previously proposed RL methods were inadequate. However, these new solution approaches still take a long time to learn how to actuate in such domains and so far are mainly researched for single task scenarios. The ability to generalize gathered knowledge and transfer it to another task has been researched for classical RL, but remains an open problem for the DRL domain. Consequently, in this article we evaluate under which conditions the application of Transfer Learning (TL) to the DRL domain improves the learning of a new task. Our results indicate that TL can greatly accelerate DRL when transferring knowledge from similar tasks, and that the similarity between tasks plays a key role in the success or failure of knowledge transfer.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127954","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: 31

A New Estimation Distributed Algorithm Applied to a Many-Objective Discrete Optimization Problem 多目标离散优化问题的一种新的估计分布算法

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.081

Glauber Botelho, André Britto, Leila Silva

引用次数: 1

Adaptive Operator Selection in NSGA-III NSGA-III自适应算子选择

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.042

Richard A. Gonçalves, C. Almeida, L. M. Pavelski, Sandra M. Venske, J. Kuk, A. Pozo

{"title":"Adaptive Operator Selection in NSGA-III","authors":"Richard A. Gonçalves, C. Almeida, L. M. Pavelski, Sandra M. Venske, J. Kuk, A. Pozo","doi":"10.1109/BRACIS.2016.042","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.042","url":null,"abstract":"Many-objective optimization (four or more objectives) presents many challenges to be considered, highlighting the need to create better algorithms prepared to deal efficiently with the increasing number of objectives. One such challenge is to determine the most efficient operator or combination of operators to be used during the optimization. In order to deal with this challenge, we propose the use of adaptive operator selection mechanisms in many-objective optimization algorithms. Two adaptive operator selection mechanisms, Adaptive Pursuit (AP) and Probability Matching (PM), are incorporated into the NSGA-III framework (a recently proposed, state-of-the-art algorithm to solve many-objective problems) to autonomously select the most suitable operator while solving a many-objective problem, according to the previous performance of each operator. The proposed algorithms, NSGA-IIIAP and NSGA-IIIPM, are tested in four different multi-objective problems from the DTLZ test suite with 3 up to 20 objectives. Statistical tests were performed to infer the significance of the results. The hypothesis that adaptive ways to select the operator to be applied during each stage of the evolutionary process is an effective way to improve the performance of the NSGA-III framework is corroborated by our results.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134022277","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

DualRadviz: Preserving Context between Classification Evaluation and Data Exploration with RadViz DualRadviz:基于RadViz的分类评估和数据探索之间的上下文保存

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.052

Igor Bueno Correa, A. Carvalho

{"title":"DualRadviz: Preserving Context between Classification Evaluation and Data Exploration with RadViz","authors":"Igor Bueno Correa, A. Carvalho","doi":"10.1109/BRACIS.2016.052","DOIUrl":"https://doi.org/10.1109/BRACIS.2016.052","url":null,"abstract":"With today's flood of data coming from many types of sources, Machine Learning becomes increasingly important. Though, many times the use of Machine Learning is not enough to make sense of all this data. This makes visualization a very useful tool for Machine Learning practitioners and data analysts alike. Interactive visualization techniques can be very helpful by giving insight on the meaning of the output from classification tasks. This also applies to the data itself, as visualization can make some characteristics of the data become clear. Several bi-dimensional projection methods have been used to visualize data instances based on their attribute values. This visualization is more difficult when the instances have a large number of attributes. One of the visualization techniques that can deal with high dimensional data is Radial Coordinates Visualization (RadViz). RadViz can also be employed to visualize the performance of a probabilistic classifier, helping a user to find problematic instances that might have been misclassified. In this study, a new approach to use RadViz is proposed and investigated. The proposed approach combines the two aforementioned uses of RadViz (attribute-based data exploration and result exploration based on the output of probabilistic classification). For such, it approach provides an easy transition between the two types of visualization. This allows the context to be preserved, since the user can visually track the same data instance from one type of visualization to the other. In order to evaluate the proposed approach, a prototype, named DualRadviz, was implemented. On this prototype, in addition to RadViz, visualization by Parallel Coordinates is also provided, so that precise instance inspection can be performed, since, different from RadViz, Parallel coordinates visualization does not suffer from ambiguity. To illustrate the usefulness of the proposed method, a case study is presented.","PeriodicalId":183149,"journal":{"name":"2016 5th Brazilian Conference on Intelligent Systems (BRACIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123548183","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

Automatic Identification of Security Risks in Edicts for Software Procurement 软件采购法令安全风险的自动识别

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI: 10.1109/BRACIS.2016.057

R. N. Peclat, G. N. Ramos

引用次数: 1