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.     

初中几何专家系统中的知识获取及实现

描述了初中几何专家系统中知识获取及实现的一般过程,指出了知识获取及实现中的难点和重点。由于研究问题的复杂性,专家系统规则库中规则量往往十分庞大,这给规则库的管理和维护带来很大不便。专家系统知识库的冗余性是影响系统运行效率和知识库维护的一个重要方面,针对一个具体的专家系统——平面几何智能解题系统,分析了关于知识库规则生成时效率低的问题,然后利用基于粗糙集的约简理论来消除和减少规则库的冗余,使得系统规则库中的规则精炼、简洁,易于维护,同时大大提高了系统的效率。     

Logistics information system auditing using expert system technology

This paper brings together two methodological strands of thinking. These are the managerial problem solving methodology of Logistics Information System auditing and the structured development of expert system technology. The investment being made in logistics in organizations is enormous and, although much effort has been devoted to creating structured methods to aid the development of information systems to support these organizations' logistics, the area of Logistics Information System auditing remains less developed.

The major aim of this paper is to provide a systemic approach of the application of expert system technology to Logistics Information System auditing. Taking a strategic view of Management Information System (MIS) in logistics, this paper describes the application of INFAUDITOR, an audit expert system, to logistics information systems auditing.

INFAUDITOR has two fundamental features. First, it covers all domains of information systems, managerial (like logistics) as well as technical aspects. Secondly, it helps to determine, in a given audit situation, the respective importance that should be given to the different audit domains and tests of control. INFAUDITOR can be viewed as consisting of several expert systems as in blackboard systems. Its fact bases include the characteristics of the enterprise, its logistics information system and the audit objectives. Its rule bases encompass the audit criteria represented as a hierarchical tree.

INFAUDITOR is used to assess the ability of a Logistics Information System (LIS) to provide decision makers with relevant, timely information for designing, planning and maintaining an efficient production system, for securing materials necessary for production, and for facilitating achievement of low operating and maintenance costs. We present and discuss results obtained by using INFAUDITOR in auditing the logistics Management Information System of a large European company.     

Approximate declarative semantics for rule base anomalies

Despite the fact that there has been a surge of publications in verification and validation of knowledge-based systems and expert systems in the past decade, there are still gaps in the study of verification and validation (V&V) of expert systems, not the least of which is the lack of appropriate semantics for expert system programming languages. Without a semantics, it is hard to formally define and analyze knowledge base anomalies such as inconsistency and redundancy, and it is hard to assess the effectiveness of V&V tools, methods and techniques that have been developed or proposed. In this paper, we develop an approximate declarative semantics for rule-based knowledge bases and provide a formal definition and analysis of knowledge base inconsistency, redundancy, circularity and incompleteness in terms of theories in the first order predicate logic. In the paper, we offer classifications of commonly found cases of inconsistency, redundancy, circularity and incompleteness. Finally, general guidelines on how to remedy knowledge base anomalies are given.     

中学智能辅导代数专家系统规则库的简化

由于研究问题的复杂性,专家系统规则库中规则量往往十分庞大,这给规则库的管理和维护带来很大不便。该文针对代数智能辅导解题系统,以规则的层次分类为基础,探讨了规则库建立过程中问题复杂度的降低,冗余规则的减少;同时应用粗集方法对其进行约简,精炼了规则库,提高了系统的效率。     

Dynamic testing of knowledge bases using the heuristic testing approach

How to develop knowledge-based and expert systems today is becoming more and more well understood; how to test these systems still poses some challenges. There has been considerable progress in developing techniques for static testing of these systems, checking for problems via formal examination methods; but there has been almost no work on dynamic testing, testing the systems under operating conditions. A novel approach for the dynamic testing of expert system rule bases is presented. This approach, Heuristic Testing, is based on the idea of first testing systems for disastrous safety and integrity problems before testing for primary functions and other classes of problems, and a prioritized series of 10 classes of faults are identified. The Heuristic Testing approach is intended to assure software reliability rather than simply find defects; the reliability is based on the 10 fault clones called compotent reliability. General procedures for conceptualizing and generating test cases were developed for all fault classes, including a Generic Testing Method for generating key test-case values. One of the classes, error-metric, illustrates how complexity-metrics, now used for predicting conventional software problems, could be developed for expert system rule bases. Two key themes are automation (automatically generating test cases) and fix-as-you-go testing (fixing a problem before continuing to test). The overall approach may be generalizable to static rule base testing, to testing of other expert system components, to testing of other nonconventional systems such as neural network and object-oriented systems, and even to conventional software.     

Performance evaluation of rule grouping on a real-time expertsystem architecture

Uses a Markov process to model a real-time expert system architecture characterized by message passing and event-driven scheduling. The model is applied to the performance evaluation of rule grouping for real-time expert systems running on this architecture. An optimizing algorithm based on Kernighan-Lin heuristic graph partitioning for the real-time architecture is developed and a demonstration system based on the model and algorithm has been developed and tested on a portion of the advanced GPS receiver (AGR) and manned manoeuvring unit (MMU) knowledge bases     

Web enabled expert systems using hyperlink-based inference

With the proliferation of the WWW, providing more intelligent Websites has become a major concern in the e-business industry. Recently, this trend has been even more accelerated by the success of Customer Relationship Management (CRM) in terms of product recommendation, and self after service, etc. As a result, many e-companies are eager to embed Web-enabled, rule-based systems, i.e. that is, expert systems, into their Websites, and several well-known commercial tools to facilitate this are already available in the market. So far, most of those tools are based on CGI, but CGI-based systems inherently suffer from problems related to overburdening, when there are too many service demands at the same time. To overcome the limitations of the existing CGI-based expert systems, we propose a new form of Web-enabled expert system that uses a hyperlink-based inference mechanism. In terms of burden to the Web server, our approach has proven to outperform the CGI-based approach theoretically as well as empirically. For practical purposes, our approach is implemented in a software system, WeBIS, and uses a graphic rule editing methodology, Expert's Diagram, that is incorporated into the system to facilitate rule generation and maintenance. WeBIS is now successfully operating for financial consultation on the Website of a leading financial consulting company in Korea.     

Overcoming deficiencies of the rule-based medical expert system

One of the current deficiencies of the rule-based expert system is its static nature. As these systems are applied to medicine, this shortcoming becomes accentuated by: the rapid speed at which new knowledge is generated, the regional differences associated with the expression of many diseases, and the rate at which patient demographics and disease incidence change over time. This research presents a solution to the static nature of the rule-based expert system by proposing a hybrid system. This system consists of an expert system and a statistical analysis system linked to a patient database. The additional feature of a rule base manager which initiates automatic database analysis to refresh the statistical correlation of each rule ensures a dynamic, current, statistically accurate rule base. The philosophical differences between data and knowledge are also addressed as they apply to this type of hybrid system. The system is then used to generate four rule bases from different knowledge sources. These rule bases are then compared.     

基于Petri网的知识库维护方法的研究

Ｐｅｔｒｉ网作为建立系统模型，性能分析，模拟，系统调度，控制等方面的工具已在计算机各方面得到了很好的应用。本文主要探讨了Ｐｅｔｒｉ网在知识库维护方面的应用，包括知识库的正确性、一致性及完备性的检查。对大型知识库的建立及其维护具有重要意义。     

The client-centred approach: expert system maintenance

Abstract: Developing an expert system is a considerable investment in time and money and yet very little attention has focused on keeping expert systems in regular beneficial use. The problems of maintaining expert systems and the lack of research that this area has attracted are major contributing factors to the continued lack of acceptance of these systems. This paper discusses the maintenance of expert systems within the context of the Client Centred Approach. Although there are many similarities between maintaining expert systems and conventional systems maintenance there is one vital difference—knowledge changes. Consequently an expert system requires regular evolutionary maintenance if its performance is not to be impaired.
This paper is intended to promote discussion of maintenance issues within the community. We review some principles and techniques that can assist the maintenance of expert systems and describe some formative work towards a maintenance methodology derived from a case study of maintaining a commercially-available expert system.     

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．     

A meta-level control architecture for production systems

Recent production system applications have been experiencing exceedingly difficult software maintenance problems. This is because the control of rule firings has been buried in the production rules themselves. To cope with this problem, we propose a meta-level control architecture for production systems, where procedural programming languages, such as Lisp and C, are employed to explicitly describe the control plans of production systems. The key idea of the architecture is to view production systems as a collection of independent rule processes, each of which monitors the global database and performs actions when its conditions are satisfied by the database. Procedural Control Macros (PCMs), which are based on C.A.R. Hoare's (1978) CSP, are then introduced into procedural programming languages to establish communication with the collection of rule processes. Although the PCMs are simple and easy to implement, the readability and maintainability of production system applications are greatly enhanced. Together with the original facilities of procedural languages, the PCMs enable users to efficiently specify the control plans for production systems. Furthermore, since control information is gathered into control plans, production rules can be declarative and thereby application-independent. This new feature makes it possible to develop large-scale shared rule bases     

Experimental results from parallel backward-chained expert systems

There are many applications which may be done by an expert system in real time, if the system is capable of real time response. the first Lisp- and Prolog-based expert systems have typically been too slow for real time response. This has lead to an effort to use other languages, the development of fast pattern matching techniques, and other methods of improving the speed of expert systems. Another approach to developing faster expert systems is to make use of the emerging parallel processing computer technology. A further use for parallelism is to allow reasonable response time for large knowledge bases. the size of knowledge bases may become as large as 20,000 chunks of knowledge (and more) in the near future in medical and space applications. This article describes the use of parallel processing in the EMYCIN backward chained rule-based model. Performance on two examples of shared memory multiprocessors is presented and contrasted with earlier simulations.     

Methods and algorithms for verification of knowledge bases in integrated expert systems

In this paper, a brief analysis is presented and methods of verification of expert systems and conventional programs are compared. The specific features of the verification of knowledge bases of integrated expert systems are shown, methods and algorithms of detecting static and dynamic anomalies in the knowledge field are described.     

利用专家系统建模——对人工智能的应用研究

专家系统是为了方便对各种问题制定决策,模拟专家的推理而设计的计算机程序。专家系统在表现那些需要制定决策的问题解决任务中尤其有效。建构专家系统规则库的学生对任一知识领域内概念之间动态的、临时的关系都会进行反思性思考。建构专家系统用到的思维方式可能是各种认知工具中最难的,因为它需要形式推理与逻辑推理,建构专家系统需要智力上的参与和挑战。     

A tool-supported approach to inter-tabular verification

The use of decision tables to verify knowledge based systems (KBS) has been advocated several times in the validation and verification (V&V) literature. However, one of the main drawbacks of these systems is that they fail to detect anomalies that occur over rule chains. In a decision table based context this means that anomalies that occur due to interactions between tables are neglected. These anomalies are called inter-tabular anomalies. In this paper we investigate an approach that deals with inter-tabular anomalies. One of the prerequisites for the approach was that it could be used by the knowledge engineer during the development of the KBS. This requires that the anomaly check can be performed on-line. As a result, the approach partly uses heuristics where exhaustive checks would be too inefficient. All detection facilities that will be described have been implemented in a table-based development tool called . The use of this tool will be briefly illustrated. In addition, some experiences in verifying large knowledge bases are discussed.     

Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems

Abstract

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.     

THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION

This paper presents results of experiments showing how machine learning methods arc useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to lest how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.     

Some applications of fuzzy logic in rule–based expert systems

Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems.