Dynamic evaluation of the development process of knowledge-based information systems

Codifying expert domain knowledge is a difficult and expensive task. To evaluate the quality of the outcome, often the same domain expert or a colleague of similar expertise is relied on to undertake a direct evaluation of the knowledge-based system or indirectly by preparing appropriate test data. During an incremental knowledge acquisition process, a data stream is available, and the knowledge base is observed and amended by an expert each time it produces an error. Using the kept record of the system’s performance, we propose an evaluation process to estimate its effectiveness as it gets evolved. We instantiate this process for an incremental knowledge acquisition methodology, Ripple Down Rules. We estimate the added value in each knowledge base update. Using these values, the decision makers in the organisation employing the knowledge-based information system can apply a cost-benefit analysis of the continuation of the incremental knowledge acquisition process. They can then determine when this process, involving keeping an expert online, should be terminated. As a result, the expert is not kept on-line longer than it is absolutely necessary. Hence, a major expense in deploying the information system—the cost of keeping a domain expert on-line—is reduced.     

USING META-PROGRAMMING,CONSTRAINT LOGIC PROGRAMMING AND APPROXIMATE REASONING FOR EXPERT SYSTEMS DESIGN

Abstract

This paper presents an enhancement of the CARESS system—A Constraint Approximative Reasoning System Support—introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives:

(1)knowledge representation and manipulation techniques and to implement them in PROLOG III, and

(2) to develop a knowledge programming environment for building expert systems. We discuss here the use of meta-programming, constraint logic programming and approximate reasoning for the design of expert systems

It has already been proven that meta-programming and logic programming are powerful techniques for expert system design. Fuzzy logic can be used to model one kind of uncertainty. Constraint logic programming is useful for dealing with the constraints given by operations using fuzzy sets.     

Constructing neural networks from expert system rules

The implementation of a PC-based expert system rules-to-neural network translator is described. Knowledge expressed as rules is translated to a neural network representation. The generated structure simulates a neural network which is able to perform as the original expert system—conclusions are drawn from user-supplied facts based on the inherent knowledge.     

多推理机实时监控软件的研究与设计

多推理机实时监控软件是一个面向实时监控的系统软件,该软件不但能够保证监控进程的可靠运行,而且能够根据系统的负载状况进程自动调度。文章引进了航天器专家诊断系统平台,把航天器专家的知识表示为推理机能够处理的知识,推理机运行时加载这些知识,并结合当前航天器运行参数状态给出诊断结果。实践证明,采用该监控软件不但可以大大提高推理机运行的稳定性,还可以优化系统资源,动态调度推理机进程,从而大大提高了航天器管理效率,节约了大量的人力物力。     

Performance evaluation of rule‐based expert systems: An example from medical billing domain

Most of the research in the area of performance evaluation of rule‐based expert systems (RBESs) is focused on verification and validation issues. Many researchers discuss usability, usefulness, portability, and response time for the evolution of RBES. The final goal of all such studies is to construct a system with optimal, accurate knowledge base. Arguably, a system with best knowledge base is actually worthless if it is never utilized in the real world. We have proposed “benefit” as a measure of evaluation and suggested some guidelines for performance evaluation of RBESs. The proposed measure has been demonstrated for performance evaluation of an RBES applied in the medical billing domain. Results showed that the system has saved hundreds of working hours during the evaluation period of 3 months. Moreover, other associated measures have also been considered. Associated measures in the medical billing domain are “claim rejection rate”—reduced by 54%—and “claim aging,” which has decreased from 34 to 28 days due to the RBES. Guidelines proposed by this research can be applied for the evaluation of expert systems implemented in other application domains, including in the first place business decision support systems.     

Finite element mesh design expert system

The paper presents a consultative rule-based expert system for finite element mesh design. The aim of the expert system presented is to propose the appropriate type of the finite elements and determine the resolution values for the finite element mesh to be used for the analysis. The extensive knowledge base, comprising about 1900 rules, was built mainly by the use of machine learning (ML) techniques. Several examples will confirm that an expert system shell written in Prolog enables efficient use of the knowledge base and adequate communication between the system and the user. The system has the ability to explain the inference process. Thus, it can also be used as a teaching tool for inexperienced users—students. The results of the experimental use of the system are encouraging and can be used as guidelines for further developments and improvements of the system.     

Knowledge based sensor fault detection for gas turbines under consideration of model based methods

The gas path analysis is a useful tool for modeling jet engines and stationary gas turbines, respectively. Such models are used for diagnosis that means that measurement values as inputs deliver state variables as outputs from the model. The measurement values are not fault free. They are disturbed by noise and by systematic sensor errors. Therefore algorithms have to be implemented which filter and compensate these errors; furthermore the modeling is not exact. Thereby the algorithms must be extended and adapted to this case. In alternation to the model based procedures the diagnosis problem can also be solved by knowledge based methods using an expert system. Such an expert system is developed and presented in this paper. It is an efficient tool — also in connection with the model based procedures — for detecting faults in the states of the engine and faults in the sensors as well. The diagnosis software package is applied on military and civilian jet engines and it is used in power plants for stationary gas turbines.     

Structures,hypergraphs, and knowledge representation in chemical engineering science. I — Problem formulation

It is shown how algebraic structure theory — a tool developed in the last two decades to describe chemical engineering systems — fits into the conceptual framework of hypergraph theory. A new type of structure — actually a special hereditary hypergraph — is introduced in order to lay the foundations for knowledge representation in expert systems designed for chemical engineering problems. Sketchy algorithms are proposed to solve the problems. An existing expert system called FOREST which is applied in the Hungarian chemical industry is mentioned and it is shown how it has been built upon the theoretical basis outlined here. Several very simple mathematical examples are shown, and a real-life example including chemical reactions, mass and heat transport between not more than two phases are treated in detail in the Appendix. A large set of open problems in modelling, mathematics and computation are presented and possible further directions are outlined.     

基于G2的实时生产调度系统研究

该文以实时专家系统工具G2作为生产调度的平台 ,通过对G2中的知识表达、知识库管理以及实时推理等关键问题的探讨 ,以某钢厂炼钢车间调度为例 ,建立调度的复合知识模型 ,最终开发一个调度的原型系统 ,并给出仿真结果     

Developmental aspects of expertise: Rationality and generalization

Successful attempts to explain expertise in human beings, or to capture its properties in expert systems, will have to contend with issues of rationality and generalization. Rationality and generalization pose enough difficulties on a purely synchronic basis. But an account of expertise must be diachronic—it must account for the development of rationality and generalization, even in those who are already experts. We describe the obstacles in the path of standard approaches to rationality and generalization, and present an alternative, interactivist treatment of rationality and its development (space forbids us to do likewise for generalization). In the interactivist account, rationality cannot be defined in general as adherence to the rules of a system of formal logic; we propose instead that rationality be understood in terms of the development of negative knowledge—knowing what kinds of errors to avoid. We examine the development of negative knowledge using examples from the history of science, and consider the consequences of an orientation towards negative knowledge for classroom instruction as well as the development of expert systems.     

Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities

In this paper, we present the architecture and describe the functionality of an Intelligent Tutoring System (ITS), which uses an expert system to make decisions during the teaching process. The expert system uses neurules for knowledge representation of the pedagogical knowledge. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The expert system consists of three components: the user modelling unit, the pedagogical unit and the inference system. The pedagogical knowledge is distributed in a number of neurule bases within the user modelling and the pedagogical unit. Another important component of the ITS, for both its development and maintenance, is its knowledge management unit, which provides knowledge acquisition and knowledge update capabilities to the system, that is, offers expert knowledge authoring capabilities to the system.     

Web-based expert systems: benefits and challenges

Convergence of technologies in the Internet and the field of expert systems have offered new ways of sharing and distributing knowledge. However, there has been a general lack of research in the area of web-based expert systems (ES). This paper addresses the issues associated with the design, development, and use of web-based ES from a standpoint of the benefits and challenges of developing and using them. The original theory and concepts in conventional ES were reviewed and a knowledge engineering framework for developing them was revisited. The study considered three web-based ES: WITS-advisor — for e-business strategy development, Fish-Expert — for fish disease diagnosis, and IMIS — to promote intelligent interviews. The benefits and challenges in developing and using ES are discussed by comparing them with traditional standalone systems from development and application perspectives.     

The role of the industrial engineer in developing expert systems

The most popular area of Artificial Intelligence application today is in expert systems. This paper contains a discussion of expert systems, otherwise known as knowledge-based systems and knowledge systems. The principal components of an expert system, and the evolution of expert systems are presented. The suitability of a task to an expert system is proposed. When a task is suitable for an expert system application, the system must be developed by a knowledge engineer. The methodology that the knowledge engineer must go through to develop an expert system is demostrated. Industrial engineers have formal training in many areas which can be useful when assumming the role of knowledge engineer. These areas of industrial engineering and how they are beneficial is discussed. What the future may hold in store is also pondered.     

关系知识表达模式及在专家系统中的应用

采用关系模式表达知识，可以利用当前流行的关系数据库管理系统（ＲＤＢＭＳ），将专家系统与ＲＤＢＭＳ上建立的管理信息系统，决策支持系统，办公自动化系统等有机结合，有利于知识的管理、存储和利用。并可在大量的管理信息，决策支持信息以及办公自动化信息中发现知识，使信息与知识共享。本文研究用关系模式表达知识的方法，并在一分类专家系统中利用ＲＤＢＭＳ强大的数据处理能力实现知识推理。     

Development of an expert system for optimizing natural gas pipeline operations

This paper presents an expert system as a decision support tool to optimize natural gas pipeline operations. A natural gas pipeline control system is a controlling system that involves many complicated operating processes. Since a dispatcher (who operates the system) might not be able to handle all of his or her tasks consistently, an expert system has been developed for optimizing the operations by providing consistent, fast and reliable decision support to the dispatcher. Consequently, inconsistency in the dispatcher's performance can be minimized. To build an expert system, the knowledge from an experienced dispatcher, who is familiar with the process in this controlling system is acquired and that knowledge has been implemented as rules in the knowledge base of the expert system. When this expert system has been validated by gas pipeline experts, it can help inexperienced dispatchers to operate the processes more effectively. The expert system is implemented on the real-time expert system shell G2 (trademark of Gensym Corp. of USA). The system also consists of a user interface that helps dispatchers visualize system conditions.     

基于Agent的专家系统推理模型

针对传统专家系统推理模型结构在知识获取方面适应性差的现状,从系统科学的视角,运用复杂适应系统理论,对传统专家系统的结构及运行机制进行了改进.引入Agent来模拟人脑中的神经元,用来承载专家系统中相互作用的知识,然后,基于Multi-Agent之间的相互作用来构建复杂适应的专家系统推理模型.从而,将专家系统中的知识获取机制、知识库、推理机三者统一于由Multi-Agent进行相互作用的复杂适应系统之中.通过设计体育赛事申办决策专家系统的原型,进行了专家系统推理模型的验证.原型运行结果表明:基于Multi-Agent的专家系统推理模型结构能够有效地提高专家系统知识获取的适应性.这为研究更加接近人脑智能的专家系统提供了崭新的研究思路.     

基于MVC模式的桥梁处治专家系统研究

专家系统(Expert system简称ES)是模拟人类专家解决问题的智能程序系统。专家系统的主要特征是有一个巨大的知识库,存储着某个专门领域的知识。在解决问题时,用户为系统提供一些已知数据,然后从系统中获得专家水平的结论[1-2]。目前专家系统已经应用到生活中的各个方面,本文着重研究了专家系统在在役桥梁中的应用,并且给出了模型建立方法、系统设计以及专家系统软件的设计流程[3]。最后将该系统应用到具体的实例中,实验证明在役桥梁专家决策系统具有良好的效果[4]。     

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.