摩托车智能设计系统的研究与开发

人工智能技术应用到摩托车设计中.根据摩托车设计知识的特点和分类,为了保证技术经验、专业知识不流失,应用面向对象的方法实现了实例知识的表示并构建了层次实例库,采用CLIPS语言实现规则知识的表示并创建了规则库,建立了实例推理和规则推理的集成推理机制.运用面向对象语言对推理结果(即设计对象)进行封装,封装的设计对象通过在方法中调用三维设计平台的二次开发函数实现参数化建模.根据上述方法开发的摩托车智能设计系统,可帮助开发人员快速地进行产品的创新设计与发展.     

基于信息熵Petri网模型的知识发现研究

为描述命题和规则的可信度,定义了命题和规则的可信度信息熵。从熵的角度研究产生式规则中的不确定性推理,应用Petri网和可信度信息熵,建立了一类新的Information Entropy Petri网模型(IEPN),介绍了不确定性知识表示和推理步骤。同时分析IEPN推理对知识发现(KDD)的指导意义,并给出了IEPN推理过程及知识发现(KDK)方法。     

知识发现在农业生产中的应用研究

文章通过对基于数据库的知识发现系统(KDD)的研究,提出了双库协同机制,它改变了KDD的结构、运行过程与机制,形成新的知识发现系统KDD。将该发现系统应用于农业领域,为合理地指导农业生产提供了科学的决策,因而具有重要的理论意义和实用价值。     

基于描述逻辑的电机故障诊断知识表示与推理

针对传统电机故障诊断专家系统中知识表示方法的不足,提出一种基于描述逻辑的电机故障诊断领域知识描述方法,并在此基础上对所构建的电机故障知识库进行了逻辑检错推理.通过对电机故障诊断领域知识进行表示和推理,可以有效地表示电机故障知识之间的关系,检测知识逻辑体系错误.在实验过程中,利用本体编辑工具Protégé采用OWL语言对其进行了实现,并通过TABLEAU算法实现了逻辑检错推理.     

一种分布式智能多主体决策支持系统模型

现代大型企业的经营活动通常分布在很大的地理范围内，甚至分布于整个世界。而在诸如商业、金融、管理等复杂领域建立决策支持系统(DSS)又需要综合考虑各种数据和知识模型，以及在该领域中发生的专业认知过程的推理模式。由此，本文提出一个分布式多主体DSS(MADSS)结构，它整合了不同类型的知识表示，并且具有任务导向的学习功能。在这个模型中，此系统由分布在各地的本地子系统组成，而子系统是基于多主体技术建立的，具有独立性和协作性。子系统由各种具有不同功能和能力的主体组成。主体具有基于用例的推理(CBR)能力和任务导向的学习能力，后者主要是通过KDD技术使其及时的在企业的数据库中挖掘有用的规则，并且通过知识评价机制提升学习结果的正确性。最后，我们用一个银行贷款受理的例子来说明这个模型。     

权限扩展RBAC模型的本体表示和实现

针对基于角色的访问控制(RBAC)模型对权限实体的刻画能力不足,提出了带权限层次扩展的RBAC模型。为结合本体在知识表示和推理方面的优势,提出了该模型的本体表示和实现方法。该方法使用Web本体语言(OWL)表示该扩展模型,借助语义Web规则语言(SWRL)定义模型中应用逻辑规则,隐式授权知识经规则推理获得。在此基础上,通过SPARQL协议和RDF查询语言(SPARQL)查询命令生成显式和隐式授权视图,实现系统安全状态分析。最后,给出了具体应用示例,表明该方法的可行性。     

知识发现中的因果关联规则挖掘研究

因果关联规则是知识库中一类重要的知识类型,具有重要的应用价值。首先对因果关系的特殊性质进行了分析,然后基于语言场和广义归纳逻辑因果模型,从表示、挖掘、评价和应用几方面,对因果关联规则的研究进行了详细论述。并在此基础上提出了隐含因果关联规则的概念。通过语言场和推理机制的运用,使因果关联规则这一重要知识形式的挖掘和评价过程具有良好的逻辑性和扩张性。     

面向复杂系统的知识发现过程模型KD(D&K)及其应用

为适应复杂系统的知识发现的需要, 在双库协同机制及其诱导的KDD* 过程模型，双基融合机制及其诱导的KDK*过程模型的基础上，借鉴协同原理，提出了将KDD* 与KDK* 有机地融合在一起的、双库协同机制与双基融合机制协同工作的知识发现过程模型KD(DK)；描述了KD(DK) 的总体流程、动态知识库系统及其特征；并在农业施肥和植保领域的应用过程中得到验证.     

面向复杂系统的知识发现过程模型KD(D&K)及其应用

为适应复杂系统的知识发现的需要,在双库协同机制及其诱导的KDD*过程模型,双基融合机制及其诱导的KDK*过程模型的基础上,借鉴协同原理,提出了将KDD*与KDK*有机地融合在一起的、双库协同机制与双基融合机制协同工作的知识发现过程模型KD(D&K);描述了KD(D&K)的总体流程、动态知识库系统及其特征;并在农业施肥和植保领域的应用过程中得到验证.     

基于支持度理论的广义Modus Ponens问题的最优解

为了将模糊推理纳入逻辑的框架并从语构和语义两个方面为模糊推理奠定严格的逻辑基础,通过将模糊推理形式化的方法移植到经典命题逻辑系统中,把FMP(fuzzy modus ponens)问题转化为GMP(generalized modus ponens)问题,并基于公式的真度概念提出了公式之间的支持度,进一步利用支持度的思想引入了GMP问题以及CGMP(collective generalized modus ponens)问题的一种新型最优求解机制.证明了最优解的存在性,同时指出,在经典命题逻辑系统中存在着与模糊逻辑完全相似的推理机制.该方法是一种程度化的方法,这就使得求解过程从算法上实现成为可能,并对知识的程度化推理有所启示.     

Sentimental causal rule discovery from Twitter

Social media, especially Twitter is now one of the most popular platforms where people can freely express their opinion. However, it is difficult to extract important summary information from many millions of tweets sent every hour. In this work we propose a new concept, sentimental causal rules, and techniques for extracting sentimental causal rules from textual data sources such as Twitter which combine sentiment analysis and causal rule discovery. Sentiment analysis refers to the task of extracting public sentiment from textual data. The value in sentiment analysis lies in its ability to reflect popularly voiced perceptions that are stated in natural language. Causal rules on the other hand indicate associations between different concepts in a context where one (or several concepts) cause(s) the other(s). We believe that sentimental causal rules are an effective summarization mechanism that combine causal relations among different aspects extracted from textual data as well as the sentiment embedded in these causal relationships. In order to show the effectiveness of sentimental causal rules, we have conducted experiments on Twitter data collected on the Kurdish political issue in Turkey which has been an ongoing heated public debate for many years. Our experiments on Twitter data show that sentimental causal rule discovery is an effective method to summarize information about important aspects of an issue in Twitter which may further be used by politicians for better policy making.     

Leveraging on causal knowledge for enhancing the root cause analysis of equipment spot inspection failures

Causal correlation data over the equipment spot-inspection operation and maintenance (O&M) records and fault investigation sheets potentially reflect the state related to the causal effect of equipment failures. Various factors influence equipment failures, making it difficult to effectively analyze the main cause of the problems. Mining and leveraging these causal data from the equipment spot inspection records will undoubtedly significantly improve the root cause analysis of the fault in the O&M system. Hence, this paper introduces causal knowledge in equipment fault O&M for the first time and proposes to exploit causal knowledge for enhancing root cause analysis of equipment spot inspection failures. Specifically, an equipment fault O&M knowledge graph with causal knowledge called CausalKG is constructed to provide knowledge support for the causal analysis of faults. That is, CausalKG consists of spot-inspection knowledge graph (SIKG) and causal relationship knowledge (CRK) in equipment fault O&M. Further, a CausalKG-ALBERT knowledge reasoning model is designed. The model transforms CausalKG into network embeddings based on relational graph convolutional networks. In turn, it combines the Q&A mechanism of the language model ALBERT to mine the root cause knowledge of equipment failures. The case study confirms that incorporating the CRK is more effective than directly using the SIKG for causality reasoning; The model can fully use causal relationship knowledge to enhance the reliability of root cause analysis. This method is valuable to help engineers strengthen their causal analysis capabilities in preventive equipment maintenance.     

基于因果规则的电力营销系统故障定位算法

现有的故障定位算法无法有效地应用于带有负载均衡机制的因果关系频繁变动的复杂系统。为此,本文提出一种基于因果规则的故障定位算法（CRFLA）。首先利用改进的因果关联兴趣度度量方法自适应地学习出故障和事件之间因果规则,然后根据得到的因果规则中故障原因集对已发生事件集的影响程度进行根因推断。该方法考虑了因果关系的同时无需明确具体的因果网络结构,并且能够灵活地结合先验知识。利用电力营销系统中真实生产环境产生的数据进行故障定位,实验结果表明,CRFLA优于传统的方法,能够迅速、有效地定位故障根因。     

A causal model for diagnostic reasoning

Up to now,there have many methods for knowledge representation and reasoning in causal networks,but few of them include the research on the coactions of nodes.In practice,ignoring these coactions may influence the accureacy of reasoning and even give rise to incorrect reasoning.In this paper,based on multilayer causal networks.the definitions on coaction nodes are given to construct a new causal network called Coaction Causal Network,which serves to construct a model of nerual network for diagnosis followed by fuzzy reasoning,and then the activation rules are given and neural computing methods are used to finish the diagnostic reasoning,These methods are proved in theory and a method of computing the number of solutions for the diagnostic reasoning is given.Finally,the experiments and the conclusions are presented.     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

基于神经网络结构学习的知识求精方法

知识求精是知识获取中必不可少的步骤．已有的用于知识求精的ＫＢＡＮＮ（ｋｎｏｗ ｌｅｄｇｅ ｂａｓｅｄ ａｒｔｉｆｉｃｉａｌｎｅｕｒａｌｎｅｔｗ ｏｒｋ）方法,主要局限性是训练时不能改变网络的拓扑结构．文中提出了一种基于神经网络结构学习的知识求精方法,首先将一组规则集转化为初始神经网络,然后用训练样本和结构学习算法训练初始神经网络,并提取求精的规则知识．网络拓扑结构的改变是通过训练时采用基于动态增加隐含节点和网络删除的结构学习算法实现的．大量实例表明该方法是有效的     

An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。