Knowledge Representation and Reasoning - Villanova University 维拉诺瓦大学知识表示与推理

名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域 二级分类 三级分类。

Knowledge-based Assessment of Behavior in DynamicEnvironmentsMichael CebullaT echnische Universität Berlin,Fakultät für Elektrotechnik und InformatikInstitut für Softwaretechnik und theoretische Informatikmce@cs.tu-berlin.deABSTRACTIn this paper we propose a knowledge-based approach for the sup-port of adaptive and context-aware behavior in autonomic systems.In order to provide meaningful and adequate behavior in the pres-ence of dynamic(or adverse)environments systems must possess the ability to assess their current state on the basis of knowledge about themselves and their environments.We propose to use on-tologies for the representation of knowledge and its integration intoa general systems architecture supporting autonomic behavior.Es-pecially we rely on fuzzy description logics in order to support ro-bust automatic reasoning w.r.t.environmental conditions.We dis-tinguish several aspects of knowledge and use multiset term rewrit-ing to describe mechanisms of coordination between them.We dis-cuss the advantages of our approach and give examples for paradig-matic applications.Categories and Subject DescriptorsI.2.4[Artificial Intelligence]:Knowledge Representation Formalisms and Methods—Representation Languages,Modal logic General TermsDesign,TheoryKeywordsContext-Awareness,Knowledge Management1.INTRODUCTIONThe topic of safety and reliability of complex systems in adverse environments currently represents a major issue in systems engi-neering.Enabled by the recent advances on thefields of hardware design,wireless communication and(last not least)the Internet the distribution of mobile devices in society will dramatically increase during the forthcoming decade.In the light of this situation new re-quirements are established concerning the robustness and reliabilityof systems.Thus,mobile systems are expected to provide safety-critical services(e.g.in thefield of medical home care)which have Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on thefirst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.KRAS’05,November5,2005,Bremen,Germany.Copyright2005ACM1-59593-202-X/05/0011...$5.00.to provide a degree of reliability and robustness w.r.t.dynamic en-vironments which is unknown in traditional applications.The main requirement related to this kind of systems is related toward their capability to automatically adapt to changing environ-ments and unexpected events.Thus they have to provide mean-ingful functionality also in situations which could not be antici-pated by their developers.Consequently these systems belong to a new type of systems which have to be context aware and to pos-sess the ability to autonomously control their behavior according to environmental conditions.This bundle of features is currently discussed under the topic autonomous computing[11].In this pre-sentation we focus on the automatic assessment of systems’be-havior claiming that this kind of system has to be aware of the degree to which its behavior conforms to situational requirements and domain-specific goals.In order to support such capabilities we choose a knowledge-based approach which enables automated inferences about situational knowledge.We claim that the role of formal methods and modeling tech-niques has considerably changed in the light of this evolution on the field of systems engineering.Traditionally formal methods were used to predict the systems’behavior during the design phase in order to verify or falsify claims concerning their behavioral prop-erties.In the future however modeling techniques will support the systems’ability regarding the assessment of its own behavior.For this sake systems have to internally represent their environment,to anticipate future developments and to initiate changes in their be-havior(e.g.by dynamic reconfiguration).In this paper we concentrate on the knowledge-based assessment of the systems’situational behavior.We claim that this capability of self-evaluation is a precondition for advanced features of au-tonomous behavior.We propose to use description logics[1]as a light-weight formalism for knowledge representation and the sup-port of automated reasoning.From our point of view such a light-weight formalism meets three requirements which may be unfamil-iar from a traditional viewpoint concerning systems specification. Intelligibility.Formal techniques are no longer exclusively con-nected to the systems’design but also play a prominent role in environmental reasoning.During runtime they have to support conceptualizations and inferences which are typical for domain experts(e.g.from medicine,disaster manage-ment or telecommunication).For this sake formal specifi-cations have to support domain specific high level concepts which are semantically rich enough to enable an adequate view on the systems environment(which is compatible with the conceptualizations of domain-experts).We claim that techniques from knowledge representations and ontological reasoning can be employed in order to incorporate relevant domain knowledge in the systems.Thus,ontological mod-eling provides an instrument for a seamless knowledge man-agement:domain knowledge can be formalized by domainexperts and integrated into systems architectures. Uncertainty and Incompleteness.Systems in adverse environments are subject to unexpected influences(uncertainty)and arecharacterized by a high complexity which makes an exactdescription impossible or inefficient.Incomplete specifica-tions make it possible to handle this situational vagueness ina robust way.As we will see the issues of vagueness and un-certainty are treated by the introduction of fuzzy logics[12]and modal logics[30]into terminological reasoning.Efficient Automated Reasoning.We claim that an enhanced in-telligibility and support for incomplete specifications have togo hand in hand with well-defined semantics and efficient de-cision procedures.A standard way of reasoning is providedprocedures which are based on tableau algorithms[1]whichsupport a smooth integration of different aspects of knowl-edge.A key issue in this context is the adequate treatmentof implicit information,e.g.with respect to structural simi-larities of different entities.Such a treatment is possible byexploiting the concept of subsumption as defined in the con-text of description logics.In this paper we present an approach for the integration of high-level concepts into complex systems which satisfies these require-ments.High-level concepts for the knowledge-based representation of and reasoning about several aspects of systems’behavior are in-tegrated into the architecture.This paper is organized as follows:first we give an outline of the general architecture(cf.Section2).Then we briefly discuss the basic concepts of fuzzy description logics(cf.Section3).Section 4–7give an overview about terminological reasoning concerning several system aspects(e.g.architecture,behavior,temporal order and knowledge).Finally we present a simple formalism of reason-ing about global behavior.2.GENERAL ARCHITECTUREMulti-Agent Systems.We base our approach on the architec-tural style of multi-agent systems[25].It is well-known that this type of system architectures support behavioral adaptivity and local decision making and thus enables meaningful behavior in highly distributed systems.Consequently we consider all relevant compo-nents of a system as agents which provide or use knowledge about the current situation.On this background our goal is to integrate knowledge about the situation and the environment into the overall architecture.Espe-cially we have to support the systems’ability to show meaning-ful reactions to unforeseen or unexpected environmental changes.. As we already argued,we use ontologies to establish the internal self-description of a system.As we will see from an architectural perspective the relevant information is gathered in a blackboard-component[24](or tuple space[5])(cf.Figure1).One distinctive feature of our approach consists in the distinc-tion of multiple knowledge aspects.In this presentation we focus on the aspects of architecture,behavior,temporal order and knowl-edge.As we will see this distinction of knowledge aspects helps us to keep the representation formalism simple and the reasoning procedure efficient(because we can re-use operators for different purposes in different contexts).Thus knowledge about complex systems is represented as an array of related sub-models(repre-sented by different knowledge bases)which each manages knowl-edge related to a certain aspect.Note that our description oftheKnowledge Basesread, write read, writeAgentsFigure1:Global Architecturearchitecture has to be regarded as a high-level treatment.Are more detailed account of such issues will be given in another paper. Aspect-related Ontologies.We define ontologies in order to provide a terminology which allows us to formulate aspect-related propositions(for a similar approach in systems architecture cf.[19]). We collect the knowledge related to the aspects in knowledge bases where the TBox contains the terminology’s definition(the ontol-ogy)while the ABox contains the extensional knowledge concern-ing the system’s state(cf.[1]).The knowledge related to the system’s state is modified from outside by modifying the terms contained in the knowledge base.As usual we use tell-,ask-and remove-routines which are provided by KB-interfaces[14](we de-liberately ignore the problems which are connected with the use of remove in this presentation[17]).As we will argue in this paper this architecture directly sup-ports automated domain-specific analysis and consistency checks. Since we organize domain-specific knowledge in knowledge bases we can apply reasoning services which are supplied by descrip-tion logic[1].Especially the reasoning about(fuzzy)subsumption supports the assessment of the current state w.r.t.to global require-ments.Note that the use of description logics directly supports reasoning about incomplete specifications(via the open world as-sumption)and the integration of implicit information(via reason-ing about subsumption).Generally we focus on a very robust style of reasoning which is supported by the use of subsumption which is capable to detect the partial fulfillment of requirements. Fuzziness.In order to further enhance this robustness of reason-ing we support the specification of vague contextual requirements. This corresponds to the habitude of domain experts to use vague specifications(e.g.formulated in natural language)in order to in-crease robustness and make information transfer more efficient.In general we use fuzzy extensions of description logics in order to provide an adequate representation of vague knowledge[27].This vagueness enhances the effectivity of knowledge processing as well as the robustness of systems’behavior.Moreover as we will see in the standard case a system’s actual state has to be evaluated w.r.t. conflicting requirements.This means that it is impossible in many situations to satisfy all requirements completely.As we will see this type of problems which is known as fuzzy optimization prob-lems[32]can be modeled using fuzzy description logics.Espe-cially the automatic evaluation of a system’s state can be supported by a fuzzy comparison of the actual state with the desired state. For this sake we heavily rely on fuzzy subsumption when reasoning about the degree to which the system’s current state conforms to (possibly conflicting)requirements.Active Knowledge.One of the great challenges in our approach concerns the management of knowledge about highly dynamic sit-uations.The knowledge-based architecture we propose has to be able to adequately react to unexpected changes in the situation.Pre:Anesthesist(a)P atient(P)expertise(a,low)Post:Monitoring(m)observes(a,m)is-connected-to(m,p)Figure2:Example:Rule for Systems Transformation This is only possible when we can presuppose the reactivity of the knowledge component(i.e.the blackboard).In order to support such a behavior we introduce rule-based knowledge into the the knowledge space.From a technical viewpoint this is done by intro-ducing inference rules(similar to LINDA-like coordination).Es-pecially the rules define relations between the different knowledge bases and thus between the different knowledge aspects.We use a rule-based calculus which enables this feature of reactive knowl-edge.Applying the Chemical Metaphor.Following[3]we use the metaphor of a chemical solution for the knowledge-based repre-sentation of a system’s state.A solution contains molecules which contain terms which are taken from the ontologies in our architec-ture.For example they may represent knowledge about systemic agents or their behavior.When these molecules meet certain crite-ria they can react according to reaction rules.Since in our model the terms are contained in multisets the semantics of reaction rules consists in multiset rewriting.We chose this highly reactive se-mantic model as the basis of our process description because we feel that it is highly appropriate for the description of unexpected behavior.Especially,environmental changes or unexpected contex-tual influences can be modeled by introducing new molecules into the solution.Systems Behavior.We use transformation rules in order to de-scribe systemic behavior.Thus,we are able to specify the interrela-tions and side effects between the different systemic aspects using coordination rules over different knowledge bases.For the sake of this presentation we introduce a simple tabular notation which describes the dynamics of knowledge.In Figure2we give a simple example for a systemic transfor-mation.According to the style of dynamic architectures we give a rule-based description of systems reconfiguration.In this case we simply describe the observation that anesthesists with lower degree of experience tend to use a monitoring device for the observation of a patient during a medical operation.Note that in the upper part of the diagram we give the tuple de-scribing the conditions which have to hold for rule application.In the lower part we describe the consequences which will be estab-lished as a result of rule application.Each tuple is composed from terms describing the state of the related knowledge bases.Conse-quently the application of a rule results in modifications of knowl-edge bases.We continue the discussion of transformation rules in Section8.Discussion.We claim that the architecture described in this sec-tion is well suited to support context aware and adaptive systems behavior.We think that our knowledge-based approach supports the management and exploitation of information on the knowledge level[18].Generally our architecture supports a knowledge based model checking.Agents can introduce requests as tuples into the global tuple space where they are interpreted as requests towards the knowledge bases.The result of the request is then stored in the tuple and thus available to the requesting agent.We think that such provision of context information supports feedback-as well as feedforward control in multiagent systems.3.FUZZY DESCRIPTION LOGICSIn order to keep this presentation self-contained we give a brief review of fuzzy description logics in this section.Following[26] we introduce semantic uncertainty by introducing multi-valued se-mantics into description logics.Consequently we have to introduce fuzzy sets[31]instead of the crisp sets used in the traditional se-mantics(cf.[1]).For this sake we conceive the model of the ter-minological knowledge which is contained in a knowledge base as fuzzy set.When used in assertional statements we can express the fact that different instances(elements of∆)may be models of a concept to a certain degree.D EFINITION1(F UZZY I NTERPRETATION).A fuzzy interpre-tation is now a pair I=(∆I,·I),where∆I is,as for the crisp case,the domain whereas·I is an interpretation function mapping1.individuals as for the crisp case,i.e.a I=b I,if a=b;2.a concept C into a membership function C I:∆I→[0,1];3.a role R into a membership function R I:∆I×∆I→[0,1].If C is a concept then C I will be interpreted as the membership degree function of the fuzzy concept C w.r.t.I.Thus if d∈∆I is an object of the domain∆I then C I(d)gives us the degree of being the object d an element of the fuzzy concept C under the interpretation I[26].The interpretation function·I has to satisfy the following equa-tions:I(d)=1⊥I(d)=0(C D)I(d)=min(C I(d),D I(d))(C D)I(d)=max(C I(d),D I(d))(¬C)I(d)=1−C I(d)(∀R.C)I(d)=inf d ∈∆I{max(1−R I(d.d ),C I(d )} (∃R.C)I(d)=sup d ∈∆I{min(R I(d.d ),C I(d )}In this article we silently introduce fuzzy numeric restrictions as well as predicates on fuzzy concrete domains(which are very sim-ilar to linguistic variables and support the integration of linguistic hedges[12]).For the description of more complex concepts we have to rely on fuzzy role chains.We also heavily rely on the con-cept of fuzzy subsumption which we introduce by example.Fuzzy Subsumption.Intuitively a concept is subsumed by an-other concept(in the crisp case)when every instance of thefirst concept is also an instance of the second.In the fuzzy case,how-ever,we are interested in the degree to which the current situation conforms to a certain concept.As an example we consider a case from the domain of disaster management.In the following we are interested in the degree to which a current-situation can be consid-ered as aflooding.flooding.=situation ∃water-level.very(High) current-situation.=situation ∃water-level.=7On this background we can reason about the following state-ment:KB|≈deg current-situation flooding1Figure3:Very HighIntuitively we can give a visual account of the argumentation related to the problem(cf.Figure3).For the linear representation of very we use:very(x)= 23x:0<x<0.752x−1:0.75≤x≤1As as solution we obtain a support of.33for the degree to which the description of the current situation is subsumed by the concept flood.We argue that this kind of request may be a typical case concerning the knowledge based support of context-awareness.4.ARCHITECTURAL KNOWLEDGEAs we already argued one of our aims consists in representing knowledge about the system’s current state and making it avail-able in complex systems’architectures.For this sake we intro-duce concepts from software architecture and integrate them into our methodological framework.In order to do this we adopt the approach outlined in[16].Especially we adopt the multiset-based description of dynamic architectures which is described there.Fol-lowing the basic decisions in our framework we represent the archi-tectural molecules as terms from an architectural ontology which are managed in a knowledge base.Thus we integrate knowledge about architecture into our architecture.Moreover since we support fuzzy ontologies we provide means for the fuzzy reasoning about systems architectures(e.g.supporting reasoning about availability or quality).Agents.In the context of multi-agent systems obviously archi-tectural knowledge has to be formulated in terms of agents.Con-sequently we have to provide an agent-related ontology.Intuitively we conceive agents as entities which are described by their senso-rial and actuatorial capabilities.We describe agents using descrip-tion logics concepts and their abilities using roles.Thus a concept describing the relevant abilities of an anesthesist can be specified as follows.anesthesist .=one vis-in.humiditytwo man-out.reactionthree vis-acc.signalexpertise.HighWe take this example from the high level modeling of complex processes.In this case we have to describe the capabilities which have to be provided by an anesthesist when monitoring a patient during a medical operation.Note that we already use fuzzy con-cepts for a robust specification of systemic requirements.Thus the identifiers one and three have to be read as fuzzy numbers related to the cardinality of role relations.Intuitively,we can state that a human agent is able to monitor only one process using his visual sense while he can oversee up to three acoustic phenomena at the same time.Expertise on the other hand is an attribute defined on a fuzzy concrete domain(High being a fuzzy value).This style of specification directly supports the comparison be-tween the capabilities of different agents.As an example we intro-duce the terminological description of an existing agent where we use real numbers in order to denote the degree of availability of ca-pabilities.These information can be used for the sake of resource planning.sp−anesthesist.=0.7vis-in.humidity0.5man-out.reaction2.3vis-acc.signalexpertise0.7Assuming that these two concept definitions are contained in(or have been inserted into)the TBox of a given knowledge base KB we use fuzzy subsumption to compute the degree to which the agent sp-anesthesist fulfills the requirements of an anesthesist.In order to do so we(or some agent)can introduce the following tuple into the global tuple space:subsumesKB(anesthesist,sp-anesthesist),degree Using this example we propose a way for the automatic assess-ment of agents using fuzzy subsumption.The computational result of this evaluation strongly depends on the selection of fuzzy opera-tors and the underlying definition of membership functions(which is omitted in the current presentation).Architectural Styles.Architectural styles contain constraints which are imposed on admissible configurations of agents.We rep-resent these constraints as terminological concepts which are con-tained in a knowledge base dedicated to architectural description. For example,when in a certain situation agents of type Anesthesist and Nurse are required the TBox of the knowledge base contains the concepts Nurse and Anesthesist.Evolution.Exploiting the obvious semantic equivalences we adopt the approach of multiset-based graph rewriting from[16] using term-rewriting on knowledge-bases.Consequently we sup-port rewriting rules which support the description of architectural transformation.For instance we can describe admissible transfor-mations as follows:Pre:Anesthesist(a)Nurse(n)P atient(p)Post:observe(a,p)observe(n,p)Using our diagrammatic notation we represent in the top part of the rule the preconditions while in the lower part the postconditions are notated.Consequently in the upper part of the table descriptions of the original architectures are given while in the bottom part fea-tures of the resulting architecture are described.Note that in either case the descriptions are inherently incomplete,which means that other features of the configurations are not relevant for the applica-tion of the rule.Figure4:State diagram for a terminology5.BEHA VIORFor the reasoning about behavior we exploit the correspondence between description logics and propositional dynamic logic[22]. This allows us to reuse the same syntactic concepts for the descrip-tion of a different systemic aspect.Intuitively we use concepts as descriptions of states while roles represent events.As an exam-ple we can use the following description of intubation which is a process in the medical setting.s0.=initiateds1.=prepareds2.=laryngoscopeds3.=prepared laryngoscopeds4.=intubateds0 ∃prepare.s1 ∃laryngoscope.s2s1 ∃laryngoscope.s3s2 ∃prepare.s3s3 ∃intubate.s4It is straightforward to represent this terminology using a state diagram(cf.Figure4).Note that this is only an excerpt of the original specification.For technical reason(not discussed here)we have to add some more definitions.Thus it is easy to see that we reuse the basic conceptual constructs from description logics for the specification of systemic aspects which are completely different. Accessibility.A crucial question in behavioral reasoning is re-lated to the accessibility of certain states.Two main questions re-lated to this topic concern the properties of liveness and safety(cf.[15]).While liveness holds when it is possible to reach a certain state a safety property holds if a certain condition never holds.In our approach we use description logic as a natural support for this kind of reasoning.When reasoning about accessibility we have to abstract from the multiple types of events which are contained in a behavioral de-scription.In order to do this we presuppose that every role name is subsumed by a most general role event(similar to so-called master modalities in modal logic).As we will see in the examples we have to suppose that the relation described by this role is transitive. Again we can use subsumption as semantic foundation for this kind of reasoning.For example we can use the following request in order to make inference concerning the possibility of intubation.subsumes KB(∃event.intubated,s0),degreeUsing this statement we retrieve if the destination state of the behavior is reachable(via a chain of arbitrary events).This is the case if the initial state s0is subsumed by a state from which a state intubated is reachable(by arbitrary occurrences of event).For safety-properties we employ the∀−operator(which is se-mantically equivalent to the2−operator of modal logics as we will see in Section7).subsumes KB(∀event.prepared,s0),degreeIf this statement holds it is impossible that a state occurs in which prepared does not hold.Consequently we can be sure that the event not prepared never occurs.Generally,we may point out that our use of fuzzy concepts sup-ports the modeling of fuzzy behavior.Consequently we can con-ceive behavioral properties(e.g.safeness or liveness)as possibilis-tic properties which are not true or false but which hold to a cer-tain degree.Again,the use of fuzzy concepts increases the expres-sive possibilities and reflects the requirements of complex systems where crisp concepts would only describe very rare and specific cases.Conformance.In many cases it is important to know if a given run(i.e.linear orders of events)conforms to a behavioral descrip-tion.In our approach we use role chains for the representation of runs.Consequently we can retrieve conformance using a statement as follows:s0 ∃prepare◦laryngoscope◦intubate.intubatedIn this section we showed how context-aware behavior can be supported by knowledge-enabled model checking about behavior. Agents can use this service in order to retrieve informations about the consequences of their actions.6.TEMPORAL ORDERA frequent issue in behavioral specification consists in the tem-poral order of event occurrences.There are many cases where we have to insist that certain events have to occur before other events in order to result in a proper way of behavior.In order to do this we define a relation about time points.From this point of view events are seen as points on a time line.We start with the defini-tion of events and pairs of events.In using binary relations about events for the specification of behavior our approach is very similar to event structures[29].D EFINITION2(P RIME E VENT S TRUCTURES).Prime event structures are defined as a structure E= E,#,≤ ,consisting of a set E of events which are partially ordered by≤,the causal de-pendency relation,and a binary,symmetric,and irreflexive relation #⊆E×E,the conflict relation which satisfy{e |e ≤e}isfinite, e#e ≤e ⇒e#e for all e,e ,e ∈E.In order to describe the progress of computations(or other be-havior)we use the notion of configurations.D EFINITION3(C ONFIGURATION).The configurations of a prime event structure are defined as a family of subsets of E,L(E) such that for all x⊆E holds∀e,e ∈x.¬(e#e )(conflict-freedom) and∀e,e .e ≤e∈x⇒e ∈x(left-closedness).Event structures are a powerful framework for the definition of semantics of process algebras.Since we integrate the basic con-cepts of this framework into our approach we are able to define。

论文收录检索证明报告华北电力大学图书馆论文作者: Zhuxiao,Wang; Wang,Zhuxiao论文发表年限: 2012-2015检索数据库: Ei Compendex检索结果 :8篇收录Title:1. An Extension of Distributed Dynamic Description Logics for the Representation of Heterogeneous Mappings2. An architecture description language based on dynamic description logics for self-healing systems3. An architecture description language based on dynamic description logics4. An Architecture Dynamic Modeling Language for Self-Healing Systems5. A Tableau-Based Reasoning Algorithm for Distributed Dynamic Description Logics6.A formal model for attack mutation using dynamic description logics7. Linear cryptanalysis and security tradeoff of block ciphering systems with channel errors8. Research on sentiment analysis in sentence and text levels with priors特此证明!(盖章)检索报告人:年月日附件:收录情况：1. Accession number: 20132016337096Title: An extension of distributed dynamic description logics for the representation of heterogeneous mappingsAuthors: Wang, Zhuxiao1 ; Guo, Jing2 ; Chen, Fei3 ; Wu, Kehe3 ; Wang, Peng4Author affiliation: 1 School of Control and Computer Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, 102206 Beijing, China2 National Computer Network Emergency Response Technical Team, Coordination Center of China, 100029 Beijing, China3 School of Control and Computer Engineering, North China Electric Power University, 102206 Beijing, China4 Institute of Information Engineering, Chinese Academy of Sciences, 100195 Beijing, China Corresponding author: Wang,Z.(****************.cn)Source title: Journal of SoftwareAbbreviated source title: J. Softw.Volume: 8Issue: 1Issue date: 2013Publication year: 2013Pages: 243-250Language: EnglishISSN: 1796217XDocument type: Journal article (JA)Publisher: Academy Publisher, P.O.Box 40,, OULU, 90571, FinlandAbstract: As a family of dynamic description logics, DDL(X) is constructed by embracing actions into the description logic X, where X represents well-studied description logics ranging from the ALC to the SHOIQ. To efficiently support automated interoperability between ontology-based information systems in distributed environments, we have to design an expressive mapping language to semantically understand resources from remote and heterogeneous systems. Distributed Dynamic Description Logics D3L(X) is a natural generalization of the DDL(X) framework, which is designed to model the distributed dynamically-changing knowledge repositories interconnected by semantic mappings and to accomplish reasoning in distributed, heterogeneous environments. In this paper, we propose an extension of Distributed Dynamic Description Logics D3L(X) and investigate the reasoning mechanisms in D3L(X). © 2013 ACADEMY PUBLISHER.Number of references: 16Main heading: Data descriptionControlled terms: Formal languages - Interoperability - Mapping - Semantics Uncontrolled terms: Distributed Dynamic Description Logics - Distributed reasonings - Dynamic description logic - Semantic mapping - Tableau algorithmClassification code: 716 Telecommunication; Radar, Radio and Television - 717 Optical Communication - 718 Telephone Systems and Related Technologies; Line Communications -723 Computer Software, Data Handling and Applications - 902.1 Engineering Graphics - 903.2 Information DisseminationDOI: 10.4304/jsw.8.1.243-250Database: CompendexCompilation and indexing terms, © 2013 Elsevier Inc.2. Accession number: 20124415623183Title: An architecture description language based on dynamic description logics for self-healing systemsAuthors: Wang, Zhuxiao1 ; Peng, Hui2 ; Guo, Jing3 ; Wu, Kehe4 ; Cui, Wenchao4 ; Wang, Xiaofeng5Author affiliation: 1 School of Control and Computer Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China2 Education Technology Center, Beijing International Studies University, Beijing 100024, China3 National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China4 School of Control and Computer Engineering, Institute of Electric Information Security Engineering Research Center of Power Information, North China Electric Power University, Beijing 102206, China5 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China Corresponding author: Wang,Z.(****************.cn)Source title: International Journal of Advancements in Computing Technology Abbreviated source title: Intl. J. Adv. Comput. Technolog.Volume: 4Issue: 18Issue date: October 2012Publication year: 2012Pages: 89-96Language: EnglishISSN: 20058039E-ISSN: 22339337Document type: Journal article (JA)Publisher: Advanced Institute of Convergence Information Technology, Myoungbo Bldg 3F,, Bumin-dong 1-ga, Seo-gu, Busan, 602-816, Korea, Republic ofAbstract: As biological systems exhibit adaptation, healing and robustness in the face of changing environmental behavior, this paradigm has actuated research dealing with the concept of self-healing systems, which attempt to heal themselves in the sense of recovering from malicious attacks and rectifying of system faults. The goal of self-healing systems is to provide survivable systems that require high dependability, robustness, adaptability, and availability. Such systems maintain one or more models, whether external or internal, at run time as a basis for identifying problems and resolving them. This article describes an architectural description language, called ADML, which is being developed as a new formal language and/or conceptual model forrepresenting evolving software architectures. The ADML embraces dynamic change as a fundamental consideration, supports a broad class of adaptive changes at the architectural level, and offers a uniform way to represent and reason about both static and dynamic aspects of self-healing systems. Because the ADML is based on the Dynamic Description Logic DDL(SHON (D)), which can represent both dynamic semantics and static semantics under a unified logical framework, architectural ontology entailment for the ADML languages can be reduced to knowledge base satisfiability in DDL(SHON (D)), and dynamic description logic algorithms and implementations can be used to provide reasoning services for ADML. In this article, we present the syntax of ADML, explain its underlying semantics using the Dynamic Description Logic DDL(SHON (D)), and exemplify our approach by applying it to the domain of load balancing a wireless remote-access system; the preliminary results certify the potential of the approach. Number of references: 14Main heading: Data descriptionControlled terms: Formal languages - Knowledge based systems - Knowledge representation - Semantics - Software architectureUncontrolled terms: Architecture description languages - Dynamic adaptations - Dynamic description logic - Knowledge representation and reasoning - Self-healing systemsClassification code: 723 Computer Software, Data Handling and Applications - 903.2 Information DisseminationDOI: 10.4156/ijact.vol4.issue 18.11Database: CompendexCompilation and indexing terms, © 2013 Elsevier Inc.3. Accession number: 20125015790730Title: An architecture description language based on dynamic description logicsAuthors: Wang, Zhuxiao1 ; Peng, Hui2 ; Guo, Jing3 ; Zhang, Ying1 ; Wu, Kehe1 ; Xu, Huan1 ; Wang, Xiaofeng4Author affiliation: 1 School of Control and Computer Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China2 Education Technology Center, Beijing International Studies University, Beijing 100024, China3 National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China4 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China Corresponding author: Wang,Z.(****************.cn)Source title: IFIP Advances in Information and Communication TechnologyAbbreviated source title: IFIP Advances in Information and Communication Technology Volume: 385 AICTMonograph title: Intelligent Information Processing VI - 7th IFIP TC 12 International Conference, IIP 2012, ProceedingsIssue date: 2012Publication year: 2012Pages: 157-166Language: EnglishISSN: 18684238ISBN-13: 9783642328909Document type: Conference article (CA)Conference name: 7th IFIP International Conference on Intelligent Information Processing, IIP 2012Conference date: October 12, 2012 - October 15, 2012Conference location: Guilin, ChinaConference code: 94249Sponsor: IFIP TC12; Guilin University of Electronic Technology; Chinese Academy of Sciences, Institute of Computing TechnologyPublisher: Springer New York, 233 Spring Street, New York, NY 10013-1578, United States Abstract: ADML is an architectural description language based on Dynamic Description Logic for defining and simulating the behavior of system architecture. ADML is being developed as a new formal language and/or conceptual model for representing the architectures of concurrent and distributed systems, both hardware and software. ADML embraces dynamic change as a fundamental consideration, supports a broad class of adaptive changes at the architectural level, and offers a uniform way to represent and reason about both static and dynamic aspects of systems. Because the ADML is based on the Dynamic Description Logic DDL( (D)), which can represent both dynamic semantics and static semantics under a unified logical framework, architectural ontology entailment for the ADML languages can be reduced to knowledge base satisfiability in DDL( (D)), and dynamic description logic algorithms and implementations can be used to provide reasoning services for ADML. In this article, we present the syntax of ADML, explain its underlying semantics using the Dynamic Description Logic DDL( (D)), and describe the core architecture description features of ADML. © 2012 IFIP International Federation for Information Processing.Number of references: 14Main heading: Data descriptionControlled terms: Formal languages - Knowledge based systems - Knowledge representation - Semantics - Software architectureUncontrolled terms: Architectural description languages - Architectural levels - Architecture description - Architecture description languages - Conceptual model - Distributed systems - Dynamic adaptations - Dynamic changes - Dynamic description logic - Dynamic semantic - Hardware and software - Knowledge base - Knowledge representation and reasoning - Logical frameworks - ON dynamics - Satisfiability - Static and dynamic - Static semantics - System architecturesClassification code: 723 Computer Software, Data Handling and Applications - 903.2 Information DisseminationDOI: 10.1007/978-3-642-32891-6_21Database: CompendexCompilation and indexing terms, © 2013 Elsevier Inc.4. Accession number: 20121214883239Title: An architecture dynamic modeling language for self-healing systemsAuthors: Wang, Zhuxiao1 ; Guo, Jing2; Wu, Kehe1; He, Hui1; Chen, Fei1Author affiliation: 1 School of Control and Computer Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China2 National Computer Network Emergency Response Technical Team, Coordination Center of China, Beijing 100029, ChinaCorresponding author: Wang,Z.(****************.cn)Source title: Procedia EngineeringAbbreviated source title: Procedia Eng.Volume: 29Monograph title: 2012 International Workshop on Information and Electronics Engineering Issue date: 2012Publication year: 2012Pages: 3909-3913Language: EnglishISSN: 18777058Document type: Conference article (CA)Conference name: 2012 International Workshop on Information and Electronics Engineering, IWIEE 2012Conference date: March 10, 2012 - March 11, 2012Conference location: Harbin, ChinaConference code: 89020Sponsor: Harbin University of Science and Technology; International Science and Engineering Research CenterPublisher: Elsevier Ltd, Langford Lane, Kidlington, Oxford, OX5 1GB, United Kingdom Abstract: As modern software-based systems increase in complexity, recovery from malicious attacks and rectification of system faults become more difficult, labor-intensive, and error-prone. These factors have actuated research dealing with the concept of self-healing systems, which employ architectural models to monitor system behavior and use inputs obtaining therefore to adapt themselves to the run-time environment. Numerous architectural description languages (ADLs) have been developed, each providing complementary capabilities for architectural development and analysis. Unfortunately, few ADLs embrace dynamic change as a fundamental consideration and support a broad class of adaptive changes at the architectural level. The Architecture Dynamic Modeling Language (ADML) is being developed as a new formal language and/or conceptual model for representing dynamic software architectures. The ADML couple the static information provided by the system requirements and the dynamic knowledge provided by tactics, and offer a uniform way to represent and reason about both static and dynamic aspects of self-healing systems. Because the ADML is based on the Dynamic Description Logic DDL, architectural ontology entailment for the ADML languages can be reduced to knowledge base satisfiability in DDL. © 2011 Published by Elsevier Ltd.Number of references: 10Main heading: ArchitectureControlled terms: Data description - Electronics engineering - Embedded systems -Formal languages - Knowledge based systems - Knowledge representation - Software architectureUncontrolled terms: Architecture description languages - Dynamic adaptation - Dynamic description logic - Knowledge representation and reasoning - Self-healing systemsClassification code: 723 Computer Software, Data Handling and Applications - 722 Computer Systems and Equipment - 718 Telephone Systems and Related Technologies; Line Communications - 717 Optical Communication - 716 Telecommunication; Radar, Radio and Television - 715 Electronic Equipment, General Purpose and Industrial - 714 Electronic Components and Tubes - 713 Electronic Circuits - 402 Buildings and TowersDOI: 10.1016/j.proeng.2012.01.593Database: CompendexCompilation and indexing terms, © 2013 Elsevier Inc.5. Accession number: 20124315590040Title: A tableau-based reasoning algorithm for distributed dynamic description logics Authors: Wang, Zhuxiao1 ; Guan, Zhitao1 ; Li, Wei1 ; Wu, Kehe1 ; Guo, Jing2 ; Tian, Guanhua3Author affiliation: 1 School of Control and Computer Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, 102206 Beijing, China2 National Computer Network Emergency Response Technical Team, Coordination Center of China, 100029 Beijing, China3 Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China Corresponding author: Wang,Z.(****************.cn)Source title: Communications in Computer and Information ScienceAbbreviated source title: Commun. Comput. Info. Sci.Volume: 307 CCISIssue: PART 1Monograph title: Information Computing and Applications - Third International Conference, ICICA 2012, ProceedingsIssue date: 2012Publication year: 2012Pages: 192-199Language: EnglishISSN: 18650929ISBN-13: 9783642340376Document type: Conference article (CA)Conference name: 3rd International Conference on Information Computing and Applications, ICICA 2012Conference date: September 14, 2012 - September 16, 2012Conference location: Chengde, ChinaConference code: 93206Sponsor: National Science Foundation of China; Hunan Institute of Engineering; YanshanUniversity; Northeastern University at Qinhuangdao; Chengde Petroleum CollegePublisher: Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany Abstract: As a family of dynamic description logics, DDL(X) is constructed by embracing actions into the description logic X, where X represents well-studied description logics ranging from the ALC to the SHOIQ. The usage of distributed computation techniques in reasoning is an important premise for the adoption of Dynamic Description Logics in a real-world setting. Practical scalability of DDL(X) reasoning inspired recently a proposal of Distributed Dynamic Description Logics (D3L) framework. D3L is a natural generalization of the DDL(X) framework, which is designed to model the distributed dynamically-changing knowledge repositories interconnected by semantic mappings and to accomplish reasoning in distributed, heterogeneous environments. In this paper, we investigate the reasoning mechanisms in D3L and propose a tableau-based reasoning algorithm for D3L, built as a composition of the state of the art tableaux reasoners for DDL(X). © 2012 Springer-Verlag.Number of references: 15Main heading: Data descriptionControlled terms: Algorithms - Formal languages - Inference engines - Semantics Uncontrolled terms: Description logic - Distributed computations - Distributed dynamics - Distributed Reasoning - Dynamic description logic - Heterogeneous environments - Knowledge repository - Natural generalization - Reasoning algorithms - Reasoning mechanism - Semantic mapping - State of the art - Tableau algorithm - Tableaux reasonersClassification code: 723 Computer Software, Data Handling and Applications - 903.2 Information Dissemination - 921 MathematicsDOI: 10.1007/978-3-642-34038-3_27Database: CompendexCompilation and indexing terms, © 2013 Elsevier Inc.6. Accession number: 20150400451745Title: A formal model for attack mutation using dynamic description logicsAuthors: Wang, Zhuxiao1 Email author ****************.cn;Guo, Jing2 Email ********************************;Shi,******************************.cn;He,Hui1Email author ***************.cn;Zhang, Ying1 Email author *******************.cn;Peng, Hui3 ***************************.cn;Tian,*************************************.cn Author affiliation: 1 School of Control and Computer Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China2 National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China3 Education Technology Center, Beijing International Studies University, Beijing, China4 Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaCorresponding author: Wang, ZhuxiaoSource title: IFIP Advances in Information and Communication TechnologyAbbreviated source title: IFIP Advances in Information and Communication Technology V olume: 432V olume title: Intelligent Information Processing VII - 8th IFIP TC 12 International Conference, IIP 2014, ProceedingsIssue date: 2014Publication year: 2014Pages: 303-311Language: EnglishISSN: 18684238ISBN-13: 9783662449790Document type: Journal article (JA)Publisher: Springer New York LLCAbstract: All currently available Network-based Intrusion Detection Systems (NIDS) rely upon passive protocol analysis which is fundamentally flawed as an attack can evade detection by exploiting ambiguities in the traffic stream as seen by the NIDS. We observe that different attack variations can be derived from the original attack using simple transformations. This paper proposes a semantic model for attack mutation based on dynamic description logics (DDL(X)), extensions of description logics (DLs) with a dynamic dimension, and explores the possibility of using DDL(X) as a basis for evasion composition. The attack mutation model describes all the possible transformations and how they can be applied to the original attack to generate a large number of attack variations. Furthermore, this paper presents a heuristics planning algorithm for the automation of evasion composition at the functional level based on DDL(X). Our approach employs classical DL-TBoxes to capture the constraints of the domain, DL-ABoxes to present the attack, and DL-formulas to encode the objective sequence of packets respectively. In such a way, the evasion composition problem is solved by a decidable tableau procedure. The preliminary results certify the potential of the approach. © IFIP International Federation for Information Processing 2014.Number of references: 9Main heading: Data descriptionControlled terms: Algorithms - Formal languages - Intrusion detection - Knowledge representation - SemanticsUncontrolled terms: Advanced evasion techniques - Dynamic description logic - Intrusion detection/prevention systems - Knowledge representation and reasoning - MultiprotocolsClassification code: 723 Computer Software, Data Handling and Applications - 903.2 Information DisseminationDatabase: CompendexCompilation and indexing terms, © 2015 Elsevier Inc.7. Accession number: 20130115855490Title: Linear cryptanalysis and security tradeoff of block ciphering systems with channel errorsAuthors: Guo, Jing1 ; Wang, Zhuxiao2Author affiliation: 1 National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC), Beijing 100029, China2 School of Control and Computer Engineering, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, ChinaCorresponding author: Guo,J.(**************************)Source title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Abbreviated source title: Lect. Notes Comput. Sci.Volume: 7645 LNCSMonograph title: Network and System Security - 6th International Conference, NSS 2012, ProceedingsIssue date: 2012Publication year: 2012Pages: 405-416Language: EnglishISSN: 03029743E-ISSN: 16113349ISBN-13: 9783642346002Document type: Conference article (CA)Conference name: 6th International Conference on Network and System Security, NSS 2012 Conference date: November 21, 2012 - November 23, 2012Conference location: Wuyishan, Fujian, ChinaConference code: 94688Publisher: Springer Verlag, Tiergartenstrasse 17, Heidelberg, D-69121, Germany Abstract: Channel errors are usually treated as an obstacle in designing an encrypted wireless system. So we are supposed to reduce them as much as possible due to the potential error bursts contributed by an avalanche effect of block ciphers. In this paper, we propose that channel errors are to be explored for the benefit of security enhancement as they could be translated to additional efforts in cryptanalysis for an adversary node. To achieve this, a system with an outer block channel coder and an inner block cipher is presented. A framework for linear cryptanalysis is established under which an eavesdropper takes advantage of linear relationship among coded symbols, as well as linear approximation of ciphers. Also presented is an analysis on the tradeoff between security enhancement and performance degradation in the presence of channel errors. © 2012 Springer-Verlag.Number of references: 13Main heading: CryptographyControlled terms: Commerce - Errors - Network securityUncontrolled terms: Avalanche effects - Block ciphers - Channel error - Coded symbols - Linear approximations - Linear cryptanalysis - Linear relationships - Performance degradation - Potential errors - Security enhancements - Wireless systems Classification code: 723 Computer Software, Data Handling and Applications - 731 Automatic Control Principles and Applications - 911.2 Industrial Economics - 921 MathematicsDOI: 10.1007/978-3-642-34601-9_31Database: CompendexCompilation and indexing terms, © 2013 Elsevier Inc.8. Accession number: 20123915464951Title: Research on sentiment analysis in sentence and text levels with priorsAuthors: He, Hui1, 3 ; Chen, Bo2 ; Wang, Zhuxiao3Author affiliation: 1 School of Control and Computer Engineering, North China Electric Power University, Beijing, China2 Beijing University of Posts and Telecommunications, Beijing, China3 Postdoctoral Working Station, China United Network Communications Group Company Limited, Beijing, ChinaCorresponding author: He,H.(****************)Source title: International Journal of Digital Content Technology and its Applications Abbreviated source title: Int. J. Digit. Content Technol. Appl.Volume: 6Issue: 15Issue date: August 2012Publication year: 2012Pages: 518-525Language: EnglishISSN: 19759339E-ISSN: 22339310Document type: Journal article (JA)Publisher: Advanced Institute of Convergence Information Technology, 707 Seokjang-dong, Gyeongju, BI Center, Room 207, Gyeongju, Gyeongbuk, 780-714, Korea, Republic of Abstract: Recently, sentiment analysis of text is becoming a hotspot in the study of natural language processing, which has drawn interesting attention due to its research value and extensive applications. This paper introduces a sentiment analysis system, which is to satisfy two aspects of sentiment analysis requirement in sentence and text levels. They are sentence orientation analysis and text sentiment retrieval. An improved Maximum Entropy (MaxEnt) with priors is used to judge the sentence orientation, which combined with Indri is used to retrieve sentiment texts according to interested terms. This sentiment analysis system has been applied in Chinese Opinion Analysis Evaluation (COAE). Promising results and analysis are presented at the end of this paper. Number of references: 26Main heading: Maximum entropy methodsControlled terms: Computational linguistics - Data mining - Information retrieval - Natural language processing systemsUncontrolled terms: Hot spot - Indri - Maximum entropy - NAtural language processing - Opinion analysis - Orientation analysis - Prior - Sentiment analysis Classification code: 716.1 Information Theory and Signal Processing - 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 723.2 Data Processing and Image Processing - 723.5 Computer ApplicationsDOI: 10.4156/jdcta.vol6.issue15.59Database: CompendexCompilation and indexing terms, © 2013 Elsevier Inc.。

人工智能基础汤晓鸥著试题英文版Artificial Intelligence Fundamentals - Exam Questions by Tang XiaoyouArtificial intelligence (AI) has emerged as a disruptive technology that promises to revolutionize various industries and aspects of human life. As we delve into the realm of AI, it becomes crucial to understand its underpinnings and applications. This article, based on the book "Artificial Intelligence Fundamentals" by Tang Xiaoyou, aims to provide a comprehensive overview of AI, followed by a series of exam questions to assess your understanding.1. Introduction to AIDefine artificial intelligence and explain its importance.Discuss the evolution of AI and its impact on society.Identify the key areas of AI research.2. Knowledge RepresentationDescribe the different types of knowledge representation techniques.Explain the concept of ontologies and their role in AI.Discuss the limitations of knowledge representation.3. Problem Solving and ReasoningDefine problem-solving techniques in AI and provide examples.Describe the difference between deductive and inductive reasoning.Explain the working principle of expert systems.4. Machine LearningDefine machine learning and classify its different types.Discuss the fundamental concepts of supervised and unsupervised learning.Explain the principles of reinforcement learning and its applications.5. Neural Networks and Deep LearningDescribe the basic structure and working principle of neural networks.Explain the concept of deep learning and its applications in AI.Discuss the advantages and disadvantages of deep learning.6. Natural Language Processing (NLP)Define NLP and its role in AI.Describe the fundamental techniques used in NLP, such as tokenization, part-of-speech tagging, and parsing.Explain the principles of machine translation and its impact on language barriers.7. Computer VisionDefine computer vision and its applications in AI.Describe the techniques used in image recognition and analysis.Discuss the working principle of object detection and its importance in various fields.8. Ethical and Social Aspects of AIDiscuss the ethical considerations in the development and deployment of AI systems.Analyze the potential social impacts of AI on employment, privacy, and security.Propose strategies to address the ethical challenges associated with AI.ConclusionArtificial intelligence, being a rapidly evolving field, offers immense opportunities and challenges. The exam questions provided in this article aim to test your understanding of the fundamental concepts and applications of AI. By answering these questions, you can assess your readiness to delve deeper into the world of AI and its potential to revolutionize our lives.人工智能基础 - 汤晓鸥著试题英文版人工智能基础——汤晓鸥著试题人工智能（AI）已成为一种颠覆性技术，有望革命性地改变各个行业和人类生活的方方面面。

accreditation 委派accuracy 准确度acquisition 获取activity patterns 活动模式added value 附加值adjacency邻接Aeolian 伊奥利亚人的, 风的, 风蚀的Age of Discovery 发现的年代aggregation聚合algorithm, definition算法,定义ambiguity 不明确analytical cartography 分析制图application programming interfaces(APIs) 应用编程接口ARCGis 美国ESRI公司开发的世界先进的地理信息系统软件ArcIMS 它是个强大的，基于标准的工具，让你快速设计和管理Internet地图服务ArcInfo 在ArcGIS软件家族中，ArcInfo是GIS软件中功能最全面的。

它包含ArcView和ArcEditor 所有功能，并加上高级空间处理和数据转换ArcNews 美国ESRI向用户终生免费赠送的ArcNews报刊ArcSDE ArcSDE在ESRI GIS软件和DBMS之间提供通道，是一个空间数据引擎ArcUser Magazine 为ESRI用户创建的报刊ArcView 桌面GIS和制图软件，提供数据可视化，查询，分析和集成功能，以及创建和编辑地理数据的能力ARPANET ARPA 计算机网（美国国防部高级研究计划局建立的计算机网）aspatial data 非空间数据？Association of Geographic Information (AGI) 地理信息协会attribute data 属性数据attributes, types 属性，类型attributive geographic data 属性地理数据autocorrelation 自相关Autodesk MapGuide 美国Autodesk公司生产的Web GIS软件Automated mapping/facility management(AM/FM) systems 自动绘图/设备管理系统facilities 设备avatars 化身A VIRIS 机载可见光/红外成像光谱仪azimuthal projections 方位投影batch vectorization 批量矢量化beer consumption 啤酒消费benchmarking 基准Berry, Brianbest fit line 最优线binary counting system 二进制计算系统binomial distribution 二项式分布bivariate Gaussian distribution 二元高斯分布block encoding 块编码Bosnia, repartitioning 波斯尼亚，再分离成两个国家buffering 缓冲区分析Borrough, PeterBusiness and service planning(retailing) application in petroleum and convenience shopping 石油和便利购物的业务和服务规划（零售）应用business drivers 业务驱动business, GIS as 业务，地理信息系统作为Buttenfield, Barbaracadasters 土地清册Callingham, Martincannibalizing 调拨Cartesian coordinate system笛卡尔坐标系Cartograms 统计地图cartographic generalization 制图综合cartographic modeling 地图建模cartometric transformations 量图变换catalog view of database 数据库目录视图census data人口普查数据Census of Population 人口普查central Place Theory 中心区位论central point rule 中点规则central tendency 中心倾向centroid 质心choropleth mapping分区制图choosing a GIS 选择一个地理信息系统class 类别classification generalization 分类综合client 客户端client-server C/S结构客户端-服务器cluster analysis 聚类分析clutter 混乱coastline weave 海岸线codified knowledge 编码知识COGO data 坐标几何数据COGO editing tools 坐标几何编辑工具Collaboration 协作Local level 地方级National level 国家级Collection-level metadata 获取级元数据Commercial-off-the-shelf (COTS) systems 成熟的商业化系统chemas-microsoft-comfficeffice" />>> Commom object request broker architecture (CORBA) 公共对象请求代理体系结构Community, GIS 社区，地理信息系统Competition 竞争Component GIS 组件地理信息系统Component object model (COM) 组件对象模型Computer assisted mass appraisal (CAMA) 辅助大量估价，>>Computer-aided design (CAD)-based GIS 基于计算机辅助制图的地理信息系统Models 数据模型Computer-aided software engineering (CASE) tool 计算机辅助软件工程工具Concatenation 串联Confidence limits 置信界限Conflation 异文合并Conformal property 等角特性Confusion matrix 混淆矩阵Conic projections 圆锥投影Connectivity 连接性Consolidation 巩固Constant term 常数项Contagious diffusion 传染扩散Continuing professional development (CPD) 持续专业发展Coordinates 坐标Copyright 版权Corridor 走廊Cost-benefit analysis 成本效益分析Cost-effectiveness evaluation 成本效率评估Counting method 计算方法Cresswell, PaulCustomer support 客户支持Cylindrical Equidistant Projection 圆柱等距投影Cylindrical projections 圆柱投影> >Dangermond, Jack 美国ESRI总裁>> dasymetric mapping 分区密度制图>>data 数据>>automation 自动化>>capture costs 获取代价>>capture project 获取工程>>collection workflow 采集工作流>> compression 压缩>>conversion 转换>>definition 定义>>geographic, nature of 地理数据，数据的性质>> GIS 地理信息系统>>industry 产业>>integration 集成>>mining 挖掘>>transfer 迁移>>translation 转化>>data model 数据模型>> definition 定义>>levels of abstraction 提取等级>> in practice 实际上>>types 类型>>database 数据库>>definition 定义>>design 设计>>generalization 综合>>global 全球的>>index 索引>>multi-user editing 多用户编辑>> structuring 结构>>database management system (DBMS) 数据库管理系统>>capabilities 能力>>data storage 数据存储>>geographic extensions 地理扩展>>types 类型>>Dayton Accord 达顿协定，1995年12月达顿协定(DAYTON ACCORD)签订,巴尔干和平已经实现,波斯尼亚(包括黑塞哥维那)再被分解成两个国家>>decision support 决策支持>>deductive reasoning 演绎推理>>definitions of GIS 地理信息系统的各种定义>>degrees of freedom 自由度>>density estimation 密度估算>>dependence in space 空间依赖>>desktop GIS 桌面地理信息系统>>desktop paradigms 桌面范例>>Digital Chart of the World (DCW) 世界数字化图>>digital divide 数字鸿沟>>Digital Earth 数字地球>>Digital elevation models (DEMs) 数字高程模型>>Digital line graph (DLG) 数字线划图>>Digital raster graphic (DRG) 数字影像图>>Digital representation 数字表现>>Digital terrain models 数字地形模型>>Digitizing 数字化>>DIME (Dual Independent Map Encoding) program 美国人口调查局建立的双重独立地图编码系统>> Dine CARE >>Discrete objects 离散对象>>Douglas-Poiker algorithm 道格拉斯-普克算法，一种矢量数据抽稀算法>>Dublin Core metadata standard 都柏林核心元数据标准>>Dynamic segmentation 动态分割>>Dynamic simulation models 动态仿真模型>>> >Easting 朝东方>>Ecological fallacy 生态谬误>>e-commerce 电子商业>>editing 编辑>>education 教育>>electromagnetic spectrum 电磁光谱>>ellipsoids 偏振光椭圆率测量仪>>of rotation 旋转的>>emergency evacuation 应急撤退>>encapsulation 封装>>environmental applications 环境应用>>environmental impact 环境影响>>epidemiology 流行病学>>equal area property 等面积特性>>Equator 赤道>>ERDAS ERDAS公司是世界上最大的专业遥感图像处理软件公司，用户遍布100多个国家，软件套数超过17000套。