2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第3页

Deep reasoning and thinking beyond deep learning by cognitive robots and brain-inspired systems 认知机器人和大脑启发系统超越深度学习的深度推理和思考

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862095

Yingxu Wang

{"title":"Deep reasoning and thinking beyond deep learning by cognitive robots and brain-inspired systems","authors":"Yingxu Wang","doi":"10.1109/ICCI-CC.2016.7862095","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862095","url":null,"abstract":"Recent basic studies reveal that AI problems are deeply rooted in both the understanding of the natural intelligence and the adoption of suitable mathematical means for rigorously modeling the brain in machine understandable forms. Learning is a cognitive process of knowledge and behavior acquisition. Learning can be classified into five categories known as object identification, cluster classification, functional regression, behavior generation, and knowledge acquisition. A fundamental challenge to knowledge learning different from the deep and recurring neural network technologies has led to the emergence of the field of cognitive machine learning on the basis of recent breakthroughs in denotational mathematics and mathematical engineering. This keynote lecture presents latest advances in formal brain studies and cognitive systems for deep reasoning and deep learning. It is recognized that key technologies enabling cognitive robots mimicking the brain rely not only on deep learning, but also on deep reasoning and thinking towards machinable thoughts and cognitive knowledge bases built by a cognitive systems. A fundamental theory and novel technology for implementing deep thinking robots are demonstrated based on concept algebra, semantics algebra, and inference algebra.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134441742","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: 21

A novel method for document summarization using Word2Vec 一种基于Word2Vec的文档摘要新方法

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862087

Zhibo Wang, Long Ma, Yanqing Zhang

{"title":"A novel method for document summarization using Word2Vec","authors":"Zhibo Wang, Long Ma, Yanqing Zhang","doi":"10.1109/ICCI-CC.2016.7862087","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862087","url":null,"abstract":"Texting mining is a process to extract useful patterns and information from large volume of unstructured text data. Unlike other quantitative data, unstructured text data cannot be directly utilized in machine learning models. Hence, data pre-processing is an essential step to remove vague or redundant data such as punctuations, stop-words, low-frequency words in the corpus, and re-organize the data in a format that computers can understand. Though existing approaches are able to eliminate some symbols and stop-words during the pre-processing step, a portion of words are not used to describe the documents' topics. These irrelevant words not only waste the storage that lessen the efficiency of computing, but also lead to confounding results. In this paper, we propose an optimization method to further remove these irrelevant words which are not highly correlated to the documents' topics. Experimental results indicate that our proposed method significantly compresses the documents, while the resulting documents remain a high discrimination in classification tasks; additionally, storage is greatly reduced according to various criteria.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134540901","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: 4

Algorithms for determining semantic relations of formal concepts by cognitive machine learning based on concept algebra 基于概念代数的形式概念语义关系的认知机器学习算法

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862021

M. Valipour, Yingxu Wang, Omar A. Zatarain, M. Gavrilova

引用次数: 3

Extracting time-oriented relationships of nutrients to losing body fat mass using inductive logic programming 利用归纳逻辑程序设计提取营养素与减少体脂量的时间导向关系

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862039

Sho Ushikubo, K. Kanamori, H. Ohwada

{"title":"Extracting time-oriented relationships of nutrients to losing body fat mass using inductive logic programming","authors":"Sho Ushikubo, K. Kanamori, H. Ohwada","doi":"10.1109/ICCI-CC.2016.7862039","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862039","url":null,"abstract":"This study was performed to extract rules for reducing body fat mass so as to prevent lifestyle-related diseases. Lifestyle-related diseases have been increasing in Japan, even among younger people. Body fat mass is related to lifestyle-related diseases. Hence, finding rules for reducing body fat mass is very meaningful. We obtained lifestyle time-series data on five male subjects who are in their 20s and not obese. The data includes the amount of body fat mass of each subject and a variety of features such as sleep, exercise, and nutrient intake. We used Inductive Logic Programming (ILP) to apply this data because ILP can more flexibly learn rules than other machine-learning methods. As a result of applying the data to ILP, our ILP system successfully extracted rules of time-oriented relationships of nutrients to decrease body fat mass based on limited data. Intake of various nutrients one day and two days prior was effective in reducing body fat mass. Moreover, we determined that nutrients related to losing body fat mass include vitamin B2, pantothenic acid, fat, vitamin B1, and biotin.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122770623","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

Modeling chunking effects on learning and performance using the Computational-Unified Learning Model (C-ULM): A multiagent cognitive process model 使用计算统一学习模型(C-ULM)建模分块对学习和绩效的影响:一个多智能体认知过程模型

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862098

D. Shell, Leen-Kiat Soh, Vlad Chiriacescu

{"title":"Modeling chunking effects on learning and performance using the Computational-Unified Learning Model (C-ULM): A multiagent cognitive process model","authors":"D. Shell, Leen-Kiat Soh, Vlad Chiriacescu","doi":"10.1109/ICCI-CC.2016.7862098","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862098","url":null,"abstract":"Chunking has emerged as a basic property of human cognition. Computationally, chunking has been proposed as a process for compressing information also has been identified in neural processes in the brain and used in models of these processes. Our purpose in this paper is to expand understanding of how chunking impacts both learning and performance using the Computational-Unified Learning Model (C-ULM) a multi-agent computational model. Chunks in C-ULM long-term memory result from the updating of concept connection weights via statistical learning. Concept connection weight values move toward the accurate weight value needed for a task and a confusion interval reflecting certainty in the weight value is shortened each time a concept is attended in working memory and each time a task is solved, and the confusion interval is lengthened when a chunk is not retrieved over a number of cycles and each time a task solution attempt fails. The dynamic tension between these updating mechanisms allows chunks to come to represent the history of relative frequency of co-occurrence for the concept connections present in the environment; thereby encoding the statistical regularities in the environment in the long-term memory chunk network. In this paper, the computational formulation of chunking in the C-ULM is described, followed by results of simulation studies examining impacts of chunking versus no chunking on agent learning and agent effectiveness. Then, conclusions and implications of the work both for understanding human learning and for applications within cognitive informatics, artificial intelligence, and cognitive computing are discussed.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125691249","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

Hebbian learning and the LMS algorithm Hebbian学习和LMS算法

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862094

B. Widrow

引用次数: 5

A key issue of semantics of information 信息语义的一个关键问题

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862093

L. Zadeh

{"title":"A key issue of semantics of information","authors":"L. Zadeh","doi":"10.1109/ICCI-CC.2016.7862093","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862093","url":null,"abstract":"In his epoch-making work on information theory, Shannon defended information in terms of entropy. Entropy-based definitions of information relate to quantity of information, but not to its meaning. Subsequent attempts to introduce semantics into information theory have made some progress but fell short of having a capability to deal with information described in natural language. This paper is aimed that of laying information for the theory which has this capability, call it a theory of semantics information (TSI). TSI is centered on a concept which plays a key role in human intelligence — A concept whose basic importance has long been and continues to be unrecognized — The concept of a restriction is pervasive in human cognition. Restrictions underlie the remarkable human ability to reason and make rational decisions in an environment of imprecision, uncertainty and incompleteness of information. Such environments are the norm in the real-world. Such environments have the traditional logical systems that become dysfunctional. There are many applications in which semantics of information plays an important role. Among such applications are: machine translation, summarization, search and decision-making under uncertainty. Informally, a restriction on a specified (focal) variable, X, written as R (X), is a statement which is a carrier of information about the values which X can take. Typically, restrictions are described in natural language. Example. X = length of time it takes to drive from Berkeley to SF Airport; R(X) = usually it takes about 90 minutes to drive from Berkeley to SF Airport. In adverse weather it may take close to 2 hours. An important issue in TSI is computation with restrictions. TSI opens the door to modes of computation in which approximation is accepted. Acceptance of approximate computations takes the calculus of restrictions (CR) into uncharted territory.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128756228","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: 10

Quantum cognitive computation by CICT CICT的量子认知计算

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862085

R. Fiorini

引用次数: 7

A feature selection framework based on supervised data clustering 一种基于监督数据聚类的特征选择框架

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862054

Hongzhi Liu, Bin Fu, Zhengshen Jiang, Zhonghai Wu, D. Hsu

引用次数: 1

On cognitive foundations of big data science and engineering 大数据科学与工程的认知基础

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862044

Yingxu Wang

引用次数: 1