智能知识获取方法研究

本文根据故障诊断专家系统及产生式规则的特点,提出了一种新的基于遗传算法的知识获取方法,通过这种方法,故障诊断专家系统的知识库可由故障事例自动生成     

基于产生式规则知识系统的设计与实现

本文针对基于产生式规则知识库系统,提出了利用关系型数据库实现的解决方案,并介绍了系统实现的主要关键技术。     

神经网络的知识获取与行为解释

神经网络的缺点之一在于它无法明显地表达其行为的含义.针对神经网络的不同结构,通过给出节点取值不同的约束条件,描述了如何从神经网络提取知识,并对神经网络的行为进行解释.举例说明了从神经网络提取产生式规划的策略.     

基于因果图的一种知识获取方法

产生式规则和因果图是知识表示的两种方法,鉴于产生式规则在表达知识和推理方面的缺陷或不足,因此寻找一种能更好地表达知识和推理的方法非常必要,而因果图具有表达知识直观,推理灵活、方便等特点。论文根据模糊式产生式规则与因果图,以及合成式模糊产生式规则与含与门、或门的因果图的对应关系,给出了将模糊产生式规则集表示的知识转换成更紧凑、直观因果图表示的方法和过程,相应的也得到了一个因果图知识的获取方法,并给了一个其转换的实例。     

对产生式规则关系数据库的实现与改进

知识库是专家系统的一个组成部分,产生式规则又是知识库的表示方法之一,本文对如何用关系数据库实现产生式规则做了详细的论述,提出了改进的办法,并给出了推理和知识库获取的程序设计算法流程图.     

一种改进的规则知识获取方法

知识获取是建立专家系统的最基本最重要的过程,但它又是研制和开发专家系统的“瓶颈”。文章提出了一种改进的规则知识机器自动获取技术,它将学习看作是在一个符号描述空间中的启发式搜索过程,能够通过归纳从专家决策的例子中确定决策规则,从而大大简化了从专家到机器的知识转换过程。     

知识获取系统NDKAS的研究与应用

知识获取系统ＮＤＫＡＳ的研究与应用潘金贵，陈彬，陈晶，陈世福（南京大学计算机科学系南京２１０００８）ＴＨＥＲＥＳＥＡＲＣＨＡＮＤＡＰＰＬＩＣＡＴＩＯＮＯＮＴＨＥＫＮＯＷＬＥＤＧＥＡＣＱＵＩＳＩＴＩＯＮＳＹＳＴＥＭ－ＮＤＫＡＳ￥ＰａｎＪｉｎｇｕｉ；Ｃｈ...     

基于数据库的知识获取系统

该文结合现有的关系数据库技术和专家系统技术,研制了一个基于关系数据库系统的知识获取系统,以使数据库为载体的知识系统的知识库管理和维护一体化。     

基于神经网络的交互式自动获取知识的N—R方法

本文为解决知识系统构造过程中瓶颈问题－－知识获取,提出了一种基于神经网络ＮＮ的自动获取多级推理产生规则的Ｎ－４方法,该方法采用了特有的ＮＮ结构模型和相应的学习算法,使得ＮＮ在学习过程中动态确定隐层节点数的同时,也产生了样例集中没有定义的新概念,学习后的ＮＮ能用本文提出的转换算法转换成推理网络,最终方便地得到了产生式规则集。     

一个面向辞典的知识获取系统的设计与实现

在知识获取中,手工填槽是一件繁琐而又枯燥的工作,效率很低。针对一类词条的处理提出了一个面向辞典的知识获取系统,通过分析辞典中文本的特征,最大可能地实现了填槽的机器自动生成,该系统分为三个子系统:词语识别子系统、规则匹配子系统、运行填槽子系统,完成了文本识别、规则匹配和运行填槽功能。     

Integrated graphical approach to knowledge representation and acquisition

An approach toward improving the accessbility of the knowledge and information structures of expert systems is described; it is based upon a foundation development environment called the Rule-Based Frame System (RBFS), which forms the kernel of a larger system, IDEAS. RBFS is a knowledge representation language, within which a distinction is drawn between information which represents the world or domain, and knowledge which states how to make conclusions based upon the domain. Information takes the form of frames, for system processing, but is presented to the user/developer as an associative network via a Visual Editor for the Generation of Associative Networks (VEGAN). Knowledge takes the form of production rules, which are connected at suitable points in the domain model, but again it is presented to the user via a graphical interface known as the Knowledge Encoding Tool (KET). KET is designed to assist in knowledge acquisition in expert systems. It uses a combination of decision support trees and associative networks as its representation. A combined use of VEGAN and KET will enable domain experts to interactively create and test their knowledge base with minimum involvement on behalf of a knowledge engineer. An inclusion of learning features in VEGAN/KET is desirable for this purpose. The main objective of these tools, therefore, is to encourage rapid prototyping by the domain expert. VEGAN and KET are implemented in the Poplog environment on SUN 3/50 workstations.     

基于改进规则引擎的农业知识推荐系统

为了使农民更加高效的获取农业知识,提出了一种基于改进规则引擎的农业知识系统.通过深入研究规则引擎的工作原理,将其引入到农业知识推荐系统中,增加了系统的正确性和准确性,使系统能够在最恰当的时间向农民提供他们最希望得到的正确的农业知识.同时根据农业领域的特点对规则引擎进行了改进,提出了规则库的树形结构化,并采用规则文件的运行前编译方式.将改进后的规则引擎应用于农业知识推荐系统中,提高了系统的效率.     

Integrated knowledge acquisition architectures

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.     

基于粗集理论的决策表知识获取方法研究

对决策表各条件分类和决策分类集合之间的关系进行了研究,提出了直接从各分类中计算决策表核及属性约简方法：依据属性约简,创建了一个多变量决策树;在此基础上,阐述了提取决策表中蕴含规则的方法,从而省去了在约简后的决策表中计算值约简步骤;通过实例,验证了这些方法的有效性。     

基于模块化设计思想的知识库设计

分析了知识库的结构和特点,根据模块化思想提出关于知识库设计和管理的几种新的尝试思想。分析表明,这几种新思想在实际的工程开发中起到了积极的作用,降低了知识库设计的复杂程度,并能有效地对知识库进行管理。     

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.     

An inexact reasoning algorithm based on fuzzy rule matrix transformations

This paper presents an efficient algorithm for dealing with inexact reasoning where fuzzy production rules are used for knowledge representation. The finiteness of the algorithm is also analyzed by means of reachability trees.     

Hierarchical fuzzy rule based systems using an information theoretic approach

This paper proposes a novel anytime algorithm for the construction of a Hierarchical Fuzzy Rule Based System using an information theoretic approach to specialise rules that do not effectively model the decision space. The amount of uncertainty tolerated within the decision provides a single tuneable parameter to control the trade off between accuracy and interpretability. The algorithm is empirically compared with existing methods of function approximation and is demonstrated on a mobile robot application in simulation.     

基于Rough集理论的分布式知识获取模型

分布式知识获取是当前数据挖掘研究领域的热点问题之一.为了利用Rough集理论荻取分布决策表中的知识,提出了一个基于Rough集理论的分布式知识获取模型,并讨论了数据在粗糙分布式环境下,运用信息抽取算子及知识生成算子获取全局决策规则的方法.这些理论与方法扩展了Rough集理论处理多数据源的知识获取问题.实例证明了这些方法的可行性.     

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the 'knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the 'paradox of expertise', the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.