基于VC++和Prolog语言混合编程的专家系统框架

为解决专家系统开发周期长的问题,提出并实现了用PROLOG与VC 混合编程方法开发专家系统框架的方案,用VC 实现专家系统框架的人机界面,用PROLOG语言构建知识库并实现推理过程.该框架具有较好的用户界面,用户只要输入或导入脚本编写的知识库、事实库和目标,就可以进行目标求解,因此简化了专家系统的开发过程.介绍了该专家系统框架的实现原理和设计思想,给出了导入导出模块、脚本语言语法检测模块和解释机制的实现方法.     

On making expert systems more like experts

Abstract: Expert systems still lack the skill of an expert when it comes to providing explanations of the results of expert reasoning. This is because while such systems may implement knowledge which is sufficient to mimic the performance of an expert, they do not necessarily model the expertise upon which that performance is based. Such a model must include knowledge of that domain's terminology, knowledge of domain facts, and knowledge of problem-solving methods. The Explainable Expert Systems project has been exploring a new paradigm for expert system development that is intended to capture such missing knowledge and make it available for explanation. This paper will discuss the principles behind this paradigm and consider two systems that employ it.     

A structural equation modeling approach to generate explanations for induced rules

The performance of an expert system depends on the quality and validity of the domain-specific knowledge built into the system. In most cases, however, domain knowledge (e.g. stock market behavior knowledge) is unstructured and differs from one domain expert to another. So, in order to acquire domain knowledge, expert system developers often take an induction approach in which a set of general rules is constructed from past examples. Expert systems based upon the induced rules were reported to perform quite well in the hold-out sample test.

However, these systems hardly provide users with an explanation which would clarify the results of a reasoning process. For this reason, users would remain unsure about whether to accept the system conclusion or not. This paper presents an approach in which explanations about the induced rules are constructed. Our approach applies the structural equation model to the quantitative data, the qualitative format of which was originally used in rule induction. This approach was implemented with Korean stock market data to show that a plausible explanation about the induced rule can be constructed.     

基于专家系统的入侵检测技术

提出了一种基于专家系统的入侵检测系统基本框架，该系统综合了基于主机和网络的两种结构特点．利用专家系统进行规则匹配技术来实现入侵检测。该技术有利于系统准确、实时地检测出攻击行为，并可给出详细的解释说明信息．而基于规则又具有模块化特征，使得知识容易封装并不断扩充，并且使系统可方便地实现对新的攻击特征的检测。     

The explanation facility and the explanation effect

The ability to explain reasoning processes used for problem solving distinguishes the expert system from other decision support systems. The explanation facility is intended to help the user critically analyze the expert system output. Studies investigating the explanation effect, however, report that the act of explaining an event's occurrence increases the perceived likelihood of the event. The consideration of expert system explanations, therefore, may lead to greater agreement with the system's output rather than the intended critical review of the output. This research examines the explanation effect resulting from the consideration of expert system explanations. The differential impact of considering expert system explanations is compared to the effect of generating written explanations. In addition, the differential impact of positive versus negative explanations is investigated. A hypothetical audit case was administered to 41 practicing auditors. An explanation effect was observed when the expert system explanation was negative. An explanation effect was not observed when the expert system explanation was positive. An explanation effect was not observed for either positive or negative self-generated explanations. Therefore, the most influential explanations were those which were expert system generated and negative or conservative in direction.     

几何构造分析专家系统—GSAES

几何构造分析是结构力学课程的重要基础知识，其特点是利用几何构造分析的基本规律，分析出几何结构的形态。几何构造分析专家系统ＧＳＡＥＳ就是用于帮助学生学习和掌握有关的几何构造分析基本规律和分析技巧的ＣＡＩ软件。该软件有四项主要功能：（１）示教功能：演示几何构造分析的基本概念、规律及该软件的使用方法；（２）获取信息：建立或选择所要分析结构的有关信息；（３）几何中构造分析：对该结构进行几何构造分析；（４）     

基于知识的装配机器人选型专家系统的研制

装配机器人的迅速发展，使装配机器人选型变得越来越重要。本文根据装配机器人进行装配业的特点，主要探讨了装配机器人选型专家系统中知识表示、推理方法以及装配知识的获取等问题。     

无人机飞控系统故障诊断专家系统设计

针对无人机飞控系统的特点和维护要求,本文介绍了MBIT故障诊断专家系统的设计思路和实现方法,构建了无人机飞控系统故障诊断专家系统的体系结构,深入研究了如何建立故障知识库,制定推理机制,组建综合数据库和故障解释等问题。并结合某型号给出了具体的示例。在工程实际应用过程中,可以利用本系统对无人机飞控系统进行有效维护。     

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.     

机械设备故障诊断专家系统的构建

使用传统的故障诊断方法只能检测单一的信号,而对于复杂系统和多元性故障诊断比较困难,所以本文选用专家系统来解决这个问题。机械设备故障诊断专家系统主要包括知识库、知识获取、推理机,综合数据库,解释模块和人机接口6部分内容。使用本专家系统可以快速找出故障的部位、机理、性质和发展趋势,帮助快速给出维修与更新决策,缩短维修和停机时间,避免造成大的经济损失。在上海—50拖拉机上进行多次测试和应用,故障诊断准确率达到95%以上,并具有很好的运行性能。