.基于知识系统的不确定性模型

研究基于知识系统（KBS）中知识的不确定性是开发KBS的重要问题,但现有模型化KBS几乎都是基于确定性知识的。以经典的CommonKADS模型为背景,采用模型化工程中的不确定性技术,研究KBS中不确定性知识的表示方法。首先在基于值系统的值集概念上引入假设函数集合的评估函数,定义静态不确定性领域知识;而后采用因果模型描述动态的不确定性推理知识和任务知识;最后将三类不确定性知识映射至CommonKADS模型。由此给出了描述不确定KBS的通用模型,扩展了KBS的可用性。     

可重用知识构件间互操作的研究与设计

设计知识构件间互操作是开发基于知识的系统(KBS)的关键,对此现有研究并不充分.以UPML可重用KBS软件体系结构为基础,采用分布式软件工程技术,设计可重用知识构件间互操作.首先将UPML适配器按操作功能分解为两个独立实现部分,并给出了一种基于知识构件间互操作的KBS体系结构;而后以匹配agent组作为任务知识-问题解决方法间互操作的载体,并设计了定位任务承担者的方法;最后以CORBA总线作为领域知识-问题解决方法问互操作的载体,并设计了一种分布式知识源的集成方式.分析结果表明,知识构件间互操作可桥连知识构件重用和KBS的体系结构重用两个抽象级别.     

高性能知识库构造环境

知识库系统(KBS)开发环境的特点是简便性和领域适应性。我们认为如果在KBS开发环境中既有抽象结均,又有多种集成工具,则该环境将会非常有效。本文介绍一个KBS 开发环境DECISION-E,它有一个适应应用领域KBS 的清晰、灵活的抽象结构,而且还有许多集成工具。这种结构使得DECISION-E能建立多种现实领域中的KBS,并使建立的KBS能较全面地反映应用领域的性质。集成工具将辅助设计者建立KBS,支持KBS 的问题求解并进行多种事务处理。在DECISIGN-E 中还采用了下面的一些特殊技术:不同层次的知识表达方法,多推理机,推理中断处理,基于全局黑板的分布式问题求解,基于知识的建模及数据抽象,演绎数据库管理方法。     

基于STEP的CAD知识表达

建立AI系统与CAD系统都能接受的统一的数据表达方式,是解决系统间失配的方法之一,由于越来越多的CAD系统一始支持STEP标准,基于STEP技术的CAD知识表达,就成为建立统一数据模型的有效方法。文章从知识建模、知识编译、物理实现等角度论述了STEP技术在知识表达领域的应用,并在此基础上阐述了KBS与CAD模块间的无缝集成技术。     

基于模型的知识建模语言

一、基于模型的知识工程方法 基于知识的系统(KBS)作为复杂软件系统,其自身及开发程遵循一定准则。早期的KBS构造基于快速原型法,即将获取的知识立即转换实现为相应的软系统,通过系统运行的反馈信息去评估、修     

大容量知识的组织质量标准及实现技术

在知识库系统中,良好的知识组织结构既能有效地改善系统的运行效率,又能方便知识库的管理、维护、共享和理解。知识组织研究是知识库系统(KBS)的重要课题之一,随着 KBS 研究和应用的发展,已经历了几个不同的阶段。在初始阶段,由于知识库能够全部地驻于内存,人们强调的是如何改进动态的知识组织结构,提高知识的查询、匹配和触发等效率;近期,大容量知识的提出,内、外存并存的组织结构已成为研究的难点。人们对知识形式的结构化,非结     

基于Multi-Agent的Web文本挖掘系统

基于Multi-Agent(多智体)技术的Web文本挖掘是从海量信息源尤其是Web文本信息源中快速、有效地获取知识所提出的一个新型研究领域。本论文给出了基于Multi-Agent技术的Web文本挖掘系统模型,并利用Multi-Agent技术实现了Web数据预处理、数据的挖掘、知识的获取、知识的评价与表示等功能。     

基于本体和语义Web服务的供应链知识集成

运用本体论、语义Web、Web服务和多Agent等技术研究供应链系统知识集成的方法，提出了一个基于本体和语义Web服务的多Agent供应链知识集成架构，讨论了供应链系统中基于语义Web服务的智能服务机制和基于元本体和共享本体的多Agent协作方法。     

基于模型的知识工程方法

知识工程技术中,基于知识的系统(KBS)的构造可看成一种知识转换过程:知识工程师从领域专家那里获取知识,并将其转化为KBS所采用的知识表达形式。例如知识工程师询问专家解决问题时使用哪些规则,再将这些以自然语言表示的规则转化为合适的KBS知识表达形式。这种方法要求知识工程师和专家从相同角度,采用共同的术语描述问题求解过程,然而,实际中知识工程师和专家所处的地位,看问题的角度是不同的。上述要求限制了基于转换观点的方法     

知识网格环境下基于本体的CBR诊断系统架构研究

提出知识网格环境下基于本体的分布式多案例推理系统架构。该架构引入基于本体的案例推理服务器和任务-方法本体,实现了过程性知识和陈述性知识分离,有效地解决了案例推理重用问题;在案例推理服务器中共享本体约束指导下,各案例库可以灵活地表达各自领域知识,实现知识集成,解决了传统案例推理系统知识局限性问题,实现了各企事业单位之间知识共享和重用。该架构是开放的并采用Web Service技术构建,能够实现网格异构环境下系统集成,具有很强的扩展性。     

The maintenance of legal knowledge based systems

Legal knowledge based systems (KBSs) are, by definition, grounded on law. Very often the relevant law is subject to routine amendment and repeal, such changes occurring at irregular and unpredictable intervals. These systems are thus particularly affected by significant problems of adaptation as a result, a fact which has limited their practical take-up. If they are to be of more practical use the maintenance issues associated with these systems must be taken seriously. In this paper we discuss the issues associated with the maintenance of legal KBSs and describe a suite of maintenance tools designed to address these issues.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

Collaborative engineering decision-making for building information channels and improving Web visibility of product manufacturers

Product manufacturers have spent the last years improving productivity and process efficiency in order to face increasingly competitive markets. Today, the visibility of technological innovations has become essential to achieve the targeted market. It is now very difficult for a product manufacturer to reach customers without owning a website that is visible on search engine results pages. The goal of this paper is to build information channels between a company and its customers through improving both a company’s content of information on the Web and its website rank on the Internet through search engine results pages. Company information and knowledge are distributed through multiple stakeholders. The problem of building information channels between a company and customers is solved through a collaborative and distributed approach, on the one hand, and is supported by decision-making tools, on the other hand. The paper proposes an engineering model for building information channels and improving the visibility of the company on the Web. Agents are used for the implementation of the approach. The proposed model and its implementation handle the requirements, constraints, functions and solutions for improving Web visibility. The prototype tool, called CAWIS (Computer Aided Web Information Sharing), examines Web visibility in real time and evaluates the performance of the proposed content of information. CAWIS allows an exploratory and open way for building information channels and improving the visibility of product manufacturers on the Web.     

Logical Object as a Basis of Knowledge Based Systems

This paper presents a framework called logical knowledge object (LKO),which is taken as a basis of the dependable development of knowledge based systems(KBSs).LKO combines logic programming and object-oriented programming paradigms,where objects are viewed as abstractions with states,constraints,behaviors and inheritance.The operational semantics defined in the style of natural semantics is simple and clear.A hybrid knowledge representation amalgamating rule,frame,semantic network and blackboard is available for both most structured and flat knowledge.The management of knowledge bases has been formally specified.Accordingly,LKO is well suited for the formal representation of knowledge and requirements of KBSs.Based on the framework,verification techniques are also explored to enhance the analysis of requirement specifications and the validation of KBSs.In addition,LKO provides a methodology for the development of KBSs,applying the concepts of rapid prototyping and top-down design to deal with changing and incomplete requirements,and to provide multiple abstract models of the domain,where formal methods might be used at each abstract level.     

A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems

A key challenge for the semantic Web is to acquire the capability to effectively query large knowledge bases. As there will be several competing systems, we need benchmarks that will objectively evaluate these systems. Development of effective benchmarks in an emerging domain is a challenging endeavor. In this paper, we propose a requirements driven framework for developing benchmarks for semantic Web knowledge base systems (SW KBSs). In this paper, we make two major contributions. First, we provide a list of requirements for SW KBS benchmarks. This can serve as an unbiased guide to both the benchmark developers and personnel responsible for systems acquisition and benchmarking. Second, we provide an organized collection of techniques and tools needed to develop such benchmarks. In particular, the collection contains a detailed guide for generating benchmark workload, defining performance metrics, and interpreting experimental results     

Goal-directed reasoning with ACE-SSM

The goal of knowledge-based systems (KBSs) is not only to produce a solution to a problem that these systems face but also to construct, implicitly or explicitly, a situation-specific model (SSM) that explicates the rationale behind that solution. This paper focuses on how KBSs can benefit from the availability of explicit goal knowledge that reflects the underlying structure (ontology) of SSMs constructed for an application task. It first shows how goal knowledge can be captured. Then, it explains how ACE-SSM-an architecture for constructing explicit SSMs-uses this knowledge to direct the construction of explicit SSMs. Finally, it discusses benefits that KBSs can derive from the availability of explicit SSMs and their underlying goal knowledge. Some of these benefits pertain to ways to simplify the construction and maintenance of KBSs through reuse, while others relate to ways to endow KBSs with more robust problem solving and explanation capabilities. These benefits are illustrated using concrete examples     

Verification of non-monotonic knowledge bases

Non-monotonic Knowledge-Based Systems (KBSs) must undergo quality assurance procedures for the following two reasons: (i) belief revision (if such is provided) cannot always guarantee the structural correctness of the knowledge base, and in certain cases may introduce new semantic errors in the revised theory; (ii) non-monotonic theories may have multiple extensions, and some types of functional errors which do not violate structural properties of a given extension are hard to detect without testing the overall performance of the KBS. This paper presents an extension of the distributed verification method, which is meant to reveal structural and functional anomalies in non-monotonic KBSs. Two classes of anomalies are considered: (i) structural anomalies which manifest themselves within a given extension (such as logical inconsistencies, structural incompleteness, and intractabilities caused by circular rule chains), and (ii) functional anomalies related to the overall performance of the KBS (such as the existence of complementary rules and some types of rule subsumptions). The corresponding verification tests are presented and illustrated on an extended example.     

Modeling in the design of a KBS validation system

We describe and illustrate the modeling issues in the design of a system for validation of knowledge based systems (KBSs). the domain of such a validation system is “KBSs and their validation problems.” the basic idea in our solution is the following. Since different KBSs may use different knowledge representation languages, we first represent the target KBS (i.e., the KBS to be validated) in a general formal model of KBS, and then validate it in this form. the advantage of this strategy is that validation problem solving needs only to refer to the common language of the general formal model. We present a set of possible conceptual abstraction levels in such a model, and argue that each level is associated with a related view on validation problems. Since high level characterizations are difficult to abstract from current knowledge representation languages, we consider the formal aspects of modeling mainly at the “lowest” level, the so-called inference primitive level. We illustrate the approach by formalizing a solution for selected modeling issues at this level.     

A hybrid system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), human experts rarely use a single type of knowledge to solve a real-world problem. A human expert usually combines a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in the intelligent systems area. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid epidemic screening KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed. The system has been tested using real epidemic screening variables and data.