专家控制系统的知识库维护

专家系绕的知识获取和表示一向是专家系统开发的“瓶颈”,而知识库的维护在大型专家系统中所占的地位更是不可忽视,实时专家系统作为专家系统的一个颇有前景的研究方向,对知识库的维护又提出了哪些新的要求呢?本文试图通过减压精馏过程智能多变量控制系统(DIMCS)~*的知识库维护,探索实时专家系统中从知识表示、知识库组织到一致性维护等一系列技术的实现方式。     

专家系统中知识库的维护

本文叙述在专家系统中如何维护知识库.主要介绍用规则表示知识的知识库如何发现某条规则有错及如何修改的问题.这是作者在研制专家系统中提出的“预备库”等新方法.知识库的维护一般应包括:一致性检查;知识的扩充、删除与修改.本文仅对以规则表示知识的知识库,介绍如何自动及半自动修改知识库中的知识的体会与做法.     

泵送混凝土专家系统知识的获取与表示

泵送混凝土专家系统是专家系统技术在泵送混凝土工程上的应用。文章重点讨论了泵送混凝土专家系统中有关知识获取和表示的技术以及与之相关的知识库技术。专家系统的核心是知识,在专家系统中把数据、公式、方法、经验以及信息等均看作为知识。专家系统基本的技术是研究知识的来源、知识的表示和运用、知识的获取和归纳等。即通过知识获取手段,将该领域专家解决特定问题的知识采用一定的知识表示技术编辑成某种特定表示形式,存放于知识库中。     

泵送混凝土专家系统知识的获取与表示

泵送混凝土专家系统是专家系统技术在泵送混凝土工程上的应用.文章重点讨论了泵送混凝土专家系统中有关知识获取和表示的技术以及与之相关的知识库技术.专家系统的核心是知识,在专家系统中把数据、公式、方法、经验以及信息等均看作为知识.专家系统基本的技术是研究知识的来源、知识的表示和运用、知识的获取和归纳等.即通过知识获取手段,将该领域专家解决特定问题的知识采用一定的知识表示技术编辑成某种特定表示形式,存放于知识库中.     

“两票”专家系统知识库建立的研究

“两票”系统是火电厂生产过程中一个重要的管理系统,结合专家系统理论建立“两票”专家系统可以切实提高“两票”管理水平,构建“两票”专家系统首先需要建立“两票”专家知识库.详细阐述了“两票”专家知识的构成,提出了对“两票”知识进行动作词、设备名称词、状态词的分解.分析了“两票”知识的结构性特点,利用XML实现“两票”专家知识的表示,利用正向最大匹配中文分词算法实现知识的获取.提出了完整的构建“两票”专家系统知识库的的方法步骤.     

球团温度场专家系统知识库的建立

阐明了知识库是球团温度场专家系统的核心和基础,介绍了球团温度场专家系统如何获取知识,如何进行知识表示以及知识存储结构,探讨了球团温度场专家系统知识库的建立方法。     

机械设计专家系统中知识的逻辑表示方法

本文给出一种表示机械设计专家系统知识的方法。这种方法在维护和修改知识库时使得有效性和完备性的检查易于实现。我们先用框架连接图(FCG)表示知识,进而用有向图(DG)理论解释,最后转换成一阶逻辑。根据一组导出的公理,应用自动推理技术,进行知识库维护与修改的有效性检查。     

行业信息化知识库系统知识库设计

行业信息化知识库(KBI)有别于一般的知识库和专家系统,所以根据需求,采用了知识本体的知识表示形式,为了更合理的建设知识库,通过对行业信息化知识的行业结构分析,构建了知识模型,并依据知识表示形式以及构建的知识模型,设计了行业信息化知识库系统的知识库.该知识库是针对战略物资行业的,提出了知识库系统的总体结构,把知识库和数据库相结合,进行了知识库结构的逐步设计.     

数据库设计的专家系统方法

本文提出一种数据库设计的新方法——专家系统方法。该设计方法是以一个基于知识的系统或专家系统为基础的。我们在 IBMPC 上用 TURBO PROLOG 语言定义,并实现了一个特定的专家系统 ERGTORS,其知识库由一个描绘应用的语义网络(或实体-关系图,简称 ERG)和一组能使系统把语义网络转换成关系数据库关系的产生式规则组成。作为例子,我们给出了用ERGTORS 作为设计工具,由给定的实体关系图生成的一组关系数据库的关系。     

数据库型知识的产生式表示

在专家系统构造中,需要大量的简单信息类知识作为推理的基础。传统的产生式表示,对于这类知识,具有很大的局限性。不仅无法给出知识之间的关系,而且知识库规模庞大。为了解决这个问题,文章引入了数据库型产生式(DatabaseStyleRule,DSR)的概念。某农业节水专家系统的实践应用表明,数据库型产生式(DSR)表示有着很好的效果,可显著降低知识库规模,提高知识之间的关联性。从而提高推理机的效率,减少知识获取的开销,增强知识库的可维护性和可扩展性。     

A semantic fuzzy expert system for a fuzzy balanced scorecard

Balanced scorecard is a widely recognized tool to support decision making in business management. Unfortunately, current balanced scorecard-based systems present two drawbacks: they do not allow to define explicitly the semantics of the underlying knowledge and they are not able to deal with imprecision and vagueness. To overcome these limitations, in this paper we propose a semantic fuzzy expert system which implements a generic framework for the balanced scorecard. In our approach, knowledge about balanced scorecard variables is represented using an OWL ontology, therefore allowing reuse and sharing of the model among different companies. The ontology acts as the basis for the fuzzy expert system, which uses highly interpretable fuzzy IF–THEN rules to infer new knowledge. Results are valuable pieces of information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be customized to adapt to different scenarios.     

The chaining syllogism in fuzzy logic

We analyze the validity of the chaining syllogism in fuzzy systems, i.e., whether two fuzzy rules IF F, THEN G, and IF G, THEN H imply the rule IF F, THEN H. Conditions are given under which this basic deduction scheme holds. "If A is predicated of all B, and B of all C, A must necessarily be predicated of all C." ;-The chaining syllogism according to Aristotle's Prior Analytics.     

Expert system for pests,diseases and weeds identification in olive crops

An expert system was developed with the aim of improving decision-making by olive oil growers. Knowledge was obtained from the literature and from experts. The knowledge was then represented in the knowledge base of the expert system in a series of IF–THEN rules. The system is supported by a data base containing information for the identification of 9 weeds, 14 insects and 14 diseases. The system is enhanced with 150 photos and drawings that assist the used in the identification process. The expert system was evaluated following the conventional expert system evaluation methodologies. According to the validation results the system was considered very satisfactory. The program can be used as an identification tool for farmers and technicians and for educational purposes.     

On the consistency of knowledge bases: the COVADIS system

It is currently thought in the knowledge-based systems (KBS) domain that sophisticated tools are necessary for helping an expert with the difficult task of knowledge acquisition. The problem of detecting inconsistencies is especially crucial. The risk of inconsistencies increases with the size of the knowledge base; for large knowledge bases, detecting inconsistencies "by hand" or even by a superficial survey of the knowledge base is impossible. Indeed, most inconsistencies are due to the interaction between several rules via often deep deductions. In this paper, we first state the problem and define our approach in the framework of classical logic. We then describe a complete method to prove the consistency (or the inconsistency) of knowledge bases that we have implemented in the COVADIS system.     

Expert system for integrated plant protection in pepper (Capsicum annuun L.)

An expert system was developed with the aim of improving decision-making by pepper growers. Knowledge was obtained from the literature and from the experts. The knowledge was then represented in the knowledge base of the expert system in a series of IF–THEN rules. The system is supported by a data base containing information for the identification of 11 weeds, 20 insects, 14 diseases, three abiotic factors and control measures. The system is enhanced with 87 photos and drawings that assist the used in the identification process and choosing control measures. The expert system was evaluated with technicians and students. According to the validation results the system was considered very satisfactory with an average rank of 9.15 by technicians and of 8.95 by students with a statistic mode ranking 10 in all the cases. The program can be used as a decision tool for farmers and technicians and for educational purposes.     

Solving conflicts in information merging by a flexible interpretation of atomic propositions

Although many techniques for merging conflicting propositional knowledge bases have already been proposed, most existing work is based on the idea that inconsistency results from the presence of incorrect pieces of information, which should be identified and removed. In contrast, we take the view in this paper that conflicts are often caused by statements that are inaccurate rather than completely false, suggesting to restore consistency by interpreting certain statements in a flexible way, rather than ignoring them completely. In accordance with this view, we propose a novel approach to merging which exploits extra-logical background information about the semantic relatedness of atomic propositions. Several merging operators are presented, which are based on different formalizations of this background knowledge, ranging from purely qualitative approaches, related to possibilistic logic, to quantitative approaches with a probabilistic flavor. Both syntactic and semantic characterizations are provided for each merging operator, and the computational complexity is analyzed.     

COMBINING KNOWLEDGE BASES CONSISTING OF FIRST-ORDER THEORIES

Consider the construction of an expert system by encoding the knowledge of different experts. Suppose the knowledge provided by each expert is encoded into a knowledge base. Then the process of combining the knowledge of these different experts is an important and nontrivial problem. We study this problem here when the expert systems are considered to be first-order theories. We present techniques for resolving inconsistencies in such knowledge bases. We also provide algorithms for implementing these techniques.     

A split-combination approach to merging knowledge bases in possibilistic logic

We propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: the splitting step and the combination step. The splitting step splits each knowledge base into two subbases and then in the second step, different classes of subbases are combined using different operators. Our merging approach is applied to knowledge bases which are self-consistent and results in a knowledge base which is also consistent. Two operators are proposed based on two different splitting methods. Both operators result in a possibilistic knowledge base which contains more information than that obtained by the t-conorm (such as the maximum) based merging methods. In the flat case, one of the operators provides a good alternative to syntax-based merging operators in classical logic. This paper is a revised and extended version of [36].     

Automatic refinement of knowledge bases with fuzzy rules

The knowledge-acquisition bottleneck obstructs the development of expert systems. Refinement of existing knowledge bases is a subproblem of the knowledge-acquisition problem. The paper presents a HEuristic REfinement System (HERES), which refines rules with mixed fuzzy and nonfuzzy concepts represented in a variant of the rule representation language Z-II automatically. HERES employs heuristics and analytical methods to guide its generation of plausible refinements. The functionality and effectiveness of HERES are verified through various case studies. It has been verified that HERES can successfully refine knowledge bases. The refinement methods can handle imprecise and uncertain examples and generate approximate rules. In this aspect, they are better than other famous learning algorithms such as ID3^15–18, AQ11, and INDUCE^{14, 19, 20} because HERES' methods are currently unique in processing inexact examples and creating approximate rules.     

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.