区熔单晶硅生产操作指导专家系统

本文介绍区熔单晶硅生产操作指导专家系统。首先，系统实现了连续图象的自动变周期、定瞬间采样，并提出一种新的区域扩张增量图象处理算法．其次，叙述了知识的获取过程及类规则，提出分布的多库结构，并实现了不确定性推理．最后，开发了区熔单晶硅生产操作指导专家系统，实现了从数据和图象采集、处理、事实获取、推理到给出操作指导一体化。     

产生式规则专家系统的原理与实现

不确定的知识表示与知识推理是专家系统研究和开发的难点。本文利用“目标驱动”方法中控制模块、规则库和事实数据库的操作原理，使用SQL Server2000和Delphi 6．0作为开发平台，通过数据格式和算法的设计构造并实现了一个产生式规则专家系统。该系统实现了不确定性知识表示和知识推理的计算机化，用户只需要为系统提供足够的已知数据，就可以获得专家水平的结论。     

基于专家系统的建筑自动化系统故障诊断

针对建筑自动化系统(BAS)故障诊断日趋困难的问题,将专家系统与故障树分析法相结合,提出一种基于专家系统的BAS故障诊断方法。采用框架和规则相结合的知识表示法,建立相应的知识库。设计基于“框架规则+不确定性推理”的推理模式,给出专家诊断系统的实现方法。实验结果表明,该系统能提高推理结果的可用性。     

基于神经网络的知识获取

本文提出了用基于规则专家系统与神经网络的集成，该系统实现了从实例中自动获取知识的功能．在产生和控制不完全情况方面提高了专家系统的推理能力．它使用无导师学习算法的神经网络来获取正规数据，并用一个符号生成器把这些正规的数据变换成规则．生成规则和训练后的神经网络作为知识库嵌于专家系统中．在诊断阶段，为了诊断不明情况，可同时使用知识库和人类专家的知识，而且系统可以利用训练过的神经网络的综合能力进行诊断，并使不相符数据完整化．     

第二代专家系统

第二代专家系统能把基于规则的启发式推理和基于问题领域模型的深层推理结合起来。它解决了现有专家系统中的许多重要问题,尤其是知识获取问题:第二代专家系统能通过检查深层推理的结果来学习新的规则。本文概述了第二代专家系统的结构并给出了一个例子。     

基于框架与规则相结合的棉纺工艺专家系统知识库的设计

本文讨论了棉纺工艺专家系统及其知识表示方法。在介绍棉纺工艺专家系统体系结构的基础之上，重点探讨了利用框架表示棉纺工艺的领域知识的具体方法、框架结构以及用框架一规则形式来表示推理规则及原理。并简要介绍了本系统基于事例的推理过程。     

基于着色Petri网模糊专家系统的研究

针对变电站无功控制模糊专家系统知识表示不确定性及规则数量多的特点,文章以模糊、着色Petri网为基础,提出了一种基于模糊着色Petri网的知识表示与规则获取方法。该方法利用Petri网的图形化环境特点,将模糊规则库的不同变量用不同的颜色加以区分,不同规则中的同一个变量用该变量的颜色集表示,构成一个模糊着色Petri网模型。充分利用着色Petri网的特点,对推理过程进行了仔细研究,并提出一种基于着色模糊Petri网的启发式搜索策略。将其用于变电站无功控制的模糊专家系统中,结果表明,基于着色Petri网的模糊知识表示和获取方法,对于大型、复杂变电站模糊专家控制系统是非常有效的。     

一种评估专家系统的研究

针对飞行器飞行状态的人工评估日趋复杂和困难的问题,将评估树技术和专家系统技术相结合,提出一种用于飞行器任务评估的综合分析模型。首先介绍系统总体结构设计,分析系统各模块的功能和联系;然后,提出一种知识表示模型以及知识获取与转换流程,建立基于数据库的知识库;最后,分析专家系统推理机制,通过不确定性推理提高系统的推理有效性。工程实践表明该专家系统是可靠有效的。     

遗传算法在故障诊断专家系统中的应用

介绍了遗传算法在故障诊断专家系统的推理和在自学习中的应用，克服了专家系统存在的推理速度慢和先验知识很少情况下知识获取困难的障碍；并将该方法应用于运输链直流调速系统，取得了良好的效果。     

网络告警知识发现研究与实现

文章研究企业网络告警数据中的知识发现问题,设计并实现了以Apriori算法为核心的网络告警关联规则发现系统。系统试运行结果表明,该系统能够有效发掘隐藏在海量告警数据背后、不易为网络管理人员所知的告警及故障模式知识。将发现的新知识应用到告警关联/故障诊断专家系统,有效突破了专家系统“知识获取”瓶颈,显著增强了专家系统推理和诊断网络故障的能力。     

Knowledge acquisition and integration tools aiming to support managerial planning in Greek SMEs

Most existing expert systems are defined in structured task domains. However, many real-life decision tasks are novel, unstructured and consequential. To support these tasks, expert systems are needed which provide an integrated environment capable of capturing new knowledge by updating the existing knowledge base. This paper describes the incremental development process of an expert system, from the initial gathering of data up to the development of knowledge acquisition tools and knowledge integration methodologies. The expert system developed addresses managerial planning tasks of Greek small-to-medium sized enterprises (SMEs). The manager sets values for parameters specifying environmental and company characteristics. The expert system responds with suggestions on feasible tactics, objectives and strategies. To cope with the changes of planning situations and also to improve the integrity of the knowledge base as the manager gains experience, knowledge acquisition tools have been introduced. These knowledge acquisition tools, which are manipulated directly by the manager, provide the system with additional knowledge and validate the knowledge already embedded in the knowledge base.     

A NEW APPROACH FOR THE INCREMENTAL DEVELOPMENT OF RETRIEVAL FUNCTIONS FOR CBR

We present a new approach to the effective development of complex retrieval components for case-based reasoning systems (CBR). Our approach goes beyond the traditional CBR approach by allowing an incremental refinement of an existing retrieval knowledge base during routine use of the system. The refinement takes place through a direct expert-system interaction while the expert is accomplishing their given tasks. We lend ideas from ripple-down rules (RDR), a proven method for the very effective and efficient acquisition of classification knowledge during the routine use of a knowledge-based system (KBS).

In our approach the expert is only required to provide explanations of why, for a given problem, a certain case should be retrieved. Incrementally a complex retrieval knowledge base as a composition of many simple retrieval functions is developed. This approach is effective with respect to both the development of highly tailored and complex retrieval knowledge bases for CBR as well as providing an intuitive and feasible approach for the expert. The approach has been implemented in our CBR system MIKAS (Menu construction using an Incremental Knowledge Acquisition System) that allows to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client.     

插件式智能麻醉系统

在手术麻醉管理信息系统中增加智能麻醉系统,利用麻醉专家知识库和最优麻醉案例知识库对麻醉方案进行检查和优化。麻醉专家知识库基于专家的经验知识和麻醉规则建成,用于检查麻醉方案;最优麻醉案例知识库基于对大量的成功案例进行挖掘而建成,用于优化麻醉方案。智能麻醉系统以插件形式整合到手术麻醉管理信息系统中,减少开发周期,降低开发成本。     

A meta-model for knowledge configuration management to support collaborative engineering

This paper focuses on industrial design and simulation processes especially in automotive and aerospace areas. Designers use business models (called expert models) such as CAD (computed aided design) and CAE (computed aided engineering) models to optimize and streamline the engineering process. Each expert model contains information such as parameters, expert rules, mathematic relations (parametric models, for example) which are shared by several users and in several different domains (mechanical, thermal, acoustic, fluid, etc.). This information is exploited at the same time in a concurrent engineering context. It is the basis of an imperfect collaboration process due to the fact that existing tools do not manage encapsulated information well and are unable to ensure that parameters and rules are consistent (same value of parameters for example) throughout different heterogeneous expert models. In this context, we propose an approach to manage knowledge using configurations synchronized with expert models which enable designers to use parameters consistently in a collaborative context. Our approach is called KCModel (knowledge configuration model): it allows acquisition, traceability, re-use and consistency of explicit knowledge used in configuration.     

A graph-based approach for rule integrity and maintainability in expert system maintenance

Just as conventional software systems have maintenance costs far exceeding development costs, so too do rule-based expert systems. They are frequently developed by an incremental and iterative method, where knowledge and decision rules are extracted and added to the system in a piecemeal manner throughout system evolution. Thus, ensuring the correctness and consistency of the rule base (RB) becomes an important, though challenging task. However, most research work in expert systems has focused on building and validating rule bases, leaving the maintenance issue unexplored. We propose a graph-based approach, called the object classification model (OCM), as a methodology for RB maintenance. An experiment was conducted to compare the OCM with traditional RB maintenance methods. The results show that the OCM helps knowledge engineers retain rule-base integrity and, thus, increase rule-base maintainability.     

A unification-based approach for knowledge base verification

Abstract: Knowledge base verification, a part of the validation process in expert system development, includes checking the knowledge base for completeness and consistency to guard against a variety of errors that can arise during the process of transferring expertise from a human expert to a computer system. Regardless of how an expert system is developed, its developers can profit from a systematic check of the knowledge base without gathering extensive data for test runs, even before the full reasoning mechanism is functioning. Until recently knowledge base verification has been largely ignored, which has led to expert systems with knowledge base errors and no safety factors for correctness. We propose a unification-based approach for verification of a knowledge base represented in the form of production rules and facts. This approach can determine conflicting, redundant, subsumed and circular rules; redundant if-conditions in rules; dead-end rules; and cycles and contradiction in rules.     

一种面向对象的模糊知识库模型

本文给出了一种专家系统模糊知识库的结构模型。重点讨论了该模型的体系结构和采用面向对象技术表示模糊规则的方法。并介绍了采用面向对象方法分析和设计模糊知识库的技术和采用面向对象串行化技术实现模糊知识库持久保存的方法。最后,分析了采用面向对象技术构建模糊知识库的优点。     

Constructing a nutrition diagnosis expert system

This paper presents a research of constructing a web-based expert system for nutrition diagnosis by utilizing the expert system techniques in artificial intelligence. The research implements Nutritional Care Process and Model (NCPM) defined by American Dietetic Association (ADA) in 2008 and integrate the nutrition diagnosis knowledge from dietetics professionals to establish the basics of building the rule-based expert system with its knowledge base. The system is built using Microsoft Visual Studio 2008 on .NET Framework 3.5SP1 utilizing the built in rule engine which comes with Windows Workflow Foundation.With the help of this system, it is easier for dietetics professionals to adapt to the newly introduced concept of nutrition diagnosis. At the heart of the web based expert system is a knowledge base, it has a rule engine which contains the nutrition diagnosis rules converted from signs and symptoms for nutrition diagnosis from dietetics professionals and are expressed in XML format which are then stored in a SQL database. A knowledge engineer will be able to use a rule editor to add new rules or to update existing rules within the rule database. Dietetics professionals would be able to enter patient’s basic data, anthropometric data, physical exam findings, biochemical data, and food/nutrition history into the program. After dietetics professionals complete nutrition assessment, the program will make inference to the rule base and make nutrition diagnosis. Dietetics professionals could then make the final diagnosis decision for the patient based on the diagnosis report generated by the web based nutrition diagnosis expert system.For this study, I have selected 100 chronic kidney disease patients under hemodialysis from a university hospital, recorded their albumin, cholesterol, creatinine before dialysis, height, and dry weight and then use these data to perform nutrition diagnosis with both the expert system and a practicing dietitian. After comparing the result, I found that the expert system is faster and more accurate than human dietitian.     

大型知识库存储结构的研究

在专家系统及其开发平台的研究中，知识库的存储和管理是一个关键问题。该文提出了多层知识单元的基本概念。基于该知识单元提出了一种基于知识节点(属性)的图矩阵、二维链表、产生式规则的三级管理模式和数据存储结构，通过知识库管理系统(KBMS)实现了二层逻辑结构和一层物理结构的三层独立映射关系。大大压缩r知识的搜索空间。经在农业专家系统综合知识库中的具体应用。该知识库系统结构的定义以及相应的KBMS完全满足上述要求，并可推广至通用的大、中型知识库系统.     

Incremental acquisition of search knowledge

The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have complete introspective access to that knowledge, their explanations of actual search considerations seem very valuable in constructing a knowledge-level model of their search processes.Furthermore, for the basis of our knowledge acquisition approach, we substantially extend the work done on Ripple-down rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. This extension allows the expert to enter his domain terms during the KA process; thus the expert provides a knowledge-level model of his search process. We call this framework nested ripple-down rules.Our approach targets the implicit representation of the less clearly definable quality criteria by allowing the expert to limit his input to the system to explanations of the steps in the expert search process. These explanations are expressed in our search knowledge interactive language. These explanations are used to construct a knowledge base representing search control knowledge. We are acquiring the knowledge in the context of its use, which substantially supports the knowledge acquisition process. Thus, in this paper, we will show that it is possible to build effective search heuristics efficiently at the knowledge level. We will discuss how our system SmS1.3 (SmS for Smart Searcher) operates at the knowledge level as originally described by Newell. We complement our discussion by employing SmS for the acquisition of expert chess knowledge for performing a highly pruned tree search. These experimental results in the chess domain are evidence for the practicality of our approach.