商业MIS框架设计辅助专家系统的知识处理方法

介绍商业ＭＩＳ框架设计辅助专家系统（ＦＧＳＭ）开发背景。讨论以面向对象的眯核心建立ＭＩＳ框加设计辅助专家系统的知识表示模型,以基于事例的推理为基本方法构造问题求解模型,给出ＦＧＳＭ系统的整体框架结构。     

大气污染总量控制规划智能决策支持系统中的知识表示和推理

在大气污染总量控制规划智能决策支持系统（ＩＳＡＰＥＣ）中知识表示方法采用产生式表示法,为提高推理的效率,将ＩＳＡＰＥＣ知识库设计为包含领域级和元级的两级知识库。为了便于用户和领域专家理解,又便于推理的实现,对这两级知识库中的知识都设计内、外两种形式,并且外部形式的知识可通过编译转化为内部形式的知识。针对领域级知识库分别设计了综合应用正向推理和逆向推理优点的目标推理机以及元推理机,此外还设计了推理中     

商业MIS框架生成系统的设计与实现

介绍了一种用于生成商业ＭＩＳ框架设计的专家系统（ＦＧＳＭ），给出了其总体结构设计、重点讨论了该系统的知识表示方法和推理系统的实现。     

锅炉故障诊断专家系统领域知识表示方法研究

选择什么样的知识表示方法以及如何恰当地把领域知识表示成工程知识,是专家系统开发中必须面对的问题。该文以电站锅炉的领域知识为研究对象,首先讨论了知识的分类及其表示方法,再根据各种表示方法的特长与不足,并结合锅炉领域知识的特征,选用产生式表示法和框架表示法进行知识表示,最后以锅炉的水冷壁管爆破为例,介绍如何把领域知识表示为工程知识。     

综合知识表示模型MAS的属性文法推理机制

根据文献［２］中提出了的基于属性文法和语义网络的综合知识表示模型ＭＡＳ，本文提出了关于实现该ＭＡＳ推理机制的基本算法，并且通过实例对该算法进行了说明，最后，证明了基于ＭＡＳ模板的属性文法是Ｌ－ＡＧ和ＩＭＡＳ的解是完全的结论。     

计算反射性ICAI模型

智能计算机辅助教学（ＩＣＡＩ）系统是一个综合多领域知识的复杂的知识系统．理想的ＩＣＡＩ应具有强的自适应性以实现个别化教育的目的．计算反射是指能因果关联地对自身进行处理的一种计算行为．本文从计算反射的基本思想出发，讨论了知识系统中计算反射的基本模型，进而推广到ＩＣＡＩ系统的设计中，提出了将教师实体和学生实体作为“自身”的反射性的ＩＣＡＩ系统的初步模型．各知识实体均由对象层和反射层组成，在不同层次上依赖不同的知识源完成不同性质的任务．     

商业MIS框架生成系统的知识表示研究

商业ＭＩＳ框架生成专家系统（ＦＧＳＭ）包含的领域知识多而染。为了简明有效地组织表达知识，文中介绍了ＦＧＳＭ中知识的分类，并详细地讨论了每类知识的具体表示方法。系统运行表明，系统中的知识易于扩展和修改，具有较强的知识组织、表达和更新能力，提高了推理效率。     

开放的分布式人工智能

本文阐明了研究开放分布式人工智能（ＤＡＩ）的重要性，详细讨论了开放ＤＡＩ系统中开放的含义，并指出开放ＤＡＩ系统中两个关键技术问题——自适应和矛盾处理及其研究现状．     

智能计算机辅助教学系统研究（续三）

智能计算机辅助教学系统研究（续三）项国雄（江西师范大学传播系南昌３３００２７）（上接１９９６年第２期）４当前及今后ＩＣＡＩ系统研究的课题随着人工智能技术以及相关理论的发展，ＩＣＡＩ的研究主要集中在以下方面：４．ｌ关于认知科学与教育科学对ＩＣＡＩ的指导...     

知识共享和重用技术与应用

知识共享和重用一直是ＤＡＩ中众多科学家和学者们研究的重要问题，也是智能制造ＩＭＳ和并行工程ＣＥ中的重要研究技术。本文从知识库结构、基于知识的系统的结构和开发模型以及知识交换语言三个方面介绍了知识共享和重要原理、方法以及在制造系统的应用。     

从知识表示到表示:人工智能认识论上的进步

知识表示是对智能进行模拟的一个数学模型,然而它可以不是一个对智能本质的描述,特别是传统的符号主义知识表示离揭示人的智能行为发生的内在过程还有很大的差距,在神经科学和心理学的指导下,通过对智能行为的生理基础和心理过程的研究,遵循“解释智能”的思想,可以得到对知识的心智表示的新认识,这种表示观的不同,预示着人工智能方法论上的进步。     

基于时序数据的模式发现算法研究

数据库中的知识发现是人工智能领域的一个重要课题,该文针对时序数据中复杂模式的问题,提出了一种新的时序序列模式的逻辑表示法,并设计出一种新的时序序列建模算法。     

Objective and subjective views of knowledge and their implications for system design

This short paper will discuss the background to the special issue through a consideration of the basic knowledge representation issues in AI. We will briefly introduce the symbolic and sub-symbolic representations which are traditionally associated with AI, before noting the socially constructed views of knowledge with which they are at odds and the techniques for knowledge elicitation which they use. This forms the context against which this special issues sits, highlighting the broad view of knowledge elicitation and representation and the issues that they raise which we feel are important to debate in the discipline. By gathering this set of papers together, we hope to encourage researchers to see the value in differing approaches and to encourage a more synthetic view of the discipline, where contributions and insights from different perspectives may be of use.     

人工智能的应用及困惑

知识获取、知识表示和知识利用成为人工智能的中心问题.但在当前的智能系统中,他们都极不完善.本文通过对人工智能的四大应用领域:专家系统、智能教学系统、自然语言理解及自动程序设计进行分析,说明了在人工智能的应用领域中困难重重.     

基于Concept—Relation模型的知识联通

1 引言 Johnson认为知识不是孤立的而是互相联系的,认知学指出思维过程体现为相关知识间的连接过程。那么如何在相关的知识之间建立联通?如何判定两条知识之间是否存在有意义的联通?又如何建立有意义的联通?这是知识处理中很重要的问题。形象地讲,知识联通就是在知识之间建立有意义的“由此及彼”的桥梁。总体上说,从知识工程角度看,要提高机器的智能程度需要知识间广泛的知识联通;从认知和语义学角度讲,知识联通是可行的。另外,Johnson的三阶段     

Handling Qualitative Aspects of Human Knowledge in Diagnosis

Knowledge involved in diagnosis of real complex systems comes from human experts and requires appropriate discrete and qualitative representation. The large amount of information resulted is difficult to be managed and prepared to enter the diagnosis system without the help of an appropriate tool. The paper proposes a knowledge elicitation scheme for multifunctional conductive flow systems under fault diagnosis along with appropriate representation of normative and faulty models. Prototype and instance manifestations get a semi-qualitative representation and symptoms refer to means-end and bond–graph entities in a new approach, suited to human diagnostician’s conceptual view. The Computer Aided Knowledge Elicitation (CAKE) tool proposed copes with knowledge involved in the diagnosis. The case study on knowledge elicitation for fault diagnosis of a hydraulic installation and the conclusions highlight advantages of the present approach.     

Automated knowledge acquisition for design and manufacturing: The case of micromachined atomizer

In order to remain competitive in the global market, original equipment manufacturers (OEMs) are developing a process-based, knowledge-driven product development environment with emphasis on the acquisition, storing, and utilization of manufacturing knowledge. This is usually achieved by using the symbolic artificial intelligence (AI) approach. Specifically, knowledge-based expert systems are developed to capture human expertise, mostly in terms of IF–THEN production rules. It has been recognized that the development of symbolic knowledge-based expert systems suffers from the so-called knowledge acquisition bottleneck. Knowledge acquisition is the process of collecting domain knowledge and transforming the knowledge into a computerized representation. It is a challenging and time-consuming process due to the difficulties involved in eliciting knowledge from human experts. This paper presents an automated approach for knowledge acquisition by integrating neural networks learning ability and fuzzy logics structured knowledge representation. Using this approach, knowledge is automatically acquired from data and represented using humanly intelligible fuzzy rules. The approach is applied to a case study of the design and manufacturing of micromachined atomizers for gas turbine engine. The influence of geometric features on the performance of the atomizers is investigated. The results are then compared with those obtained using traditional regression analysis approach (abstract mathematical models). It was found that the automated approach provides an efficient means for knowledge acquisition. Since the fuzzy rules extracted are easy to understand, they can be used to allow more clear specification of manufacturing processes and to shorten learning curves for novice manufacturing engineers.     

Knowledge acquisition using hypertext

This paper addresses the issue of supporting knowledge acquisition using hypertext. We propose a way of tightly integrating hypertext and structured object representation, using Artificial Intelligence (AI) frames for the basic representation of hypertext nodes. Epistemologically, a dual view of the resulting space is of interest. One view is that of hypertext which emphasizes nodes containg g text, including formal knowledge representation. The other view focuses on objects with certain relationships, which define a semantic network. Both in hypertext and in semantic networks the relations between chunks of knowledge are explicitly represented by links. However, in today's hypertext systems a node typically contains just informal text and references to other nodes. Our approach additionally facilitates the explicit representation of structure “inside” hypertext nodes using partitions. We show the usefulness of such a tight integration for knowledge acquisition, providing several features useful for supporting it based on a level of basic hypertext functionality. In particular, we sketch a method for doing knowledge acquisition in such an environment. Hypertext is used as a mediating “semiformal” representation, which allows experts to directly represent knowledge without the immediate support of knowledge engineers. These help then to make this knowledge operational, supported by the system's facility to provide templates as well as their links to the semiformal representation. As an example of our results of using this method of knowledge acquisition, we illustrate the strategic knowledge in our application domain. More generally, our approach supports important aspects of (software) engineering knowledge-based systems and their maintenance. Also their user interface can be improved this way.     

Rough集理论中知识与运算的矩阵表示

同一问题在不同知识表示下算法难度不同。Rough集理论把知识定义为对对象的分类能力,并提供了一套基于代数系统的知识表达和处理方法。然而在代数表示下,知识的本质以及运算直观性较差,不易于理解。同济大学苗夺谦教授建立了知识与信息之间的关系,在此基础上给出了Rough集理论中概念和运算的信息表示,并给出了知识约简在代数和信息两种表示下的等价性证明。现进一步将知识及其运算表示成粒矩阵形式,继而给出了知识约简在代数、信息和粒矩阵3种表示下的等价性证明。