Minimizing maintenance anomalies in expert systems

Expert systems are emerging as a powerful technology for solving many problems previously requiring human experts. However, maintenance has been identified as a major difficulty in expert system implementations. Surprisingly, the problem of maintenance has only recently begun to receive attention in expert systems research, though it has long been an issue in databases. Databases are in a constant state of change, and the prevention of maintenance anomalies is essential. As similar maintenance operations are performed on rule bases, this paper investigates techniques to avoid maintenance anomalies in expert system rule bases. The result is an expert system rule base structure that is appropriate for volatile production use. In addition to lower maintenance demands, this approach favorably impacts on verification, computational efficiency, and storage requirements.     

A test of the usefulness of surveys in identifying potential expert systems applications in tax planning

Before expert systems can be developed to assist in tax practice, hundreds of tasks must be analyzed to determine which are most appropriate for system development. The purpose of this research are to gain an understanding of the tax planning applications domain and to test the validity of using a questionnaire in task selection. The most critical subset of the planning tasks was analyzed concerning characteristics that would indicate expertise. A questionnaire was used to obtain feedback from firm tax partners about the appropriateness of the subset of planning tasks for expert system development. The data gave a preliminary indication that corporate alternative minimum tax planning was the most viable task. A panel of tax expert systems specialists concurred. Further study of the questionnaire use was recommended.     

Shop-floor scheduling at shipbuilding yards using the multiple intelligent agent system

The intelligent agent system has become a typical approach to research distributed artificial intelligence and distributed problem solving. However, despite its various technical advantages, the distribution and expansion of the intelligent agent system approach has been limited because existing methodologies rely on specialized applications and therefore require intensive investment to develop new systems. In an attempt to conquer these difficulties, a standardized methodology to construct intelligent agent systems is proposed. This approach deviates from current agent system approaches of repeatedly constructing new and customized expert systems. Specifically, it provides an economical method for developing intelligent agent systems by investigating the possibility of standardizing message communication protocols in linguistics speech-act theory and by supplementing traditional algorithmic systems with intelligent segments using, among others, expert system tools. To verify effectiveness, the shop-floor scheduling system of a large-scale shipbuilding yard has been redesigned, developed, and tested using this approach. This shop-floor scheduling system requires the scheduling of when and where to process block construction under various constraints. It is a difficult four-dimensional time and space allocation problem involving traditional NP-complete search spaces. The tested intelligent agent system proposes an innovative method for reducing the search space into three levels: the algorithm level, the agent intelligence level, and the level of cooperation among agents. It also provides methods for solving deadlock occurrence and non-uniformity problems resulting from parallel processing. Test results demonstrate applicability and economy, among other technical advantages.     

An expert system approach to fault diagnosis in hydraulic systems

Abstract: Increased interest in problem-solving decisions in the area of engineering leads to an increased demand for expert system applications. This paper presents an expert system that detects faults in hydraulic systems by simulating the reasoning of the expert, using as guide the hydraulic circuit of the system on the screen and extensive graphical information. A graphical user interface, appropriate explanations by using hypertext and user modelling have been implemented in order to produce a system that overcomes the usual interface problems of expert systems in engineering.
This system has been developed in close collaboration with the company Automation Systems SA (Mannesmann Rexroth — Greece) and is currently used to detect faults in hydraulic presses, a fact that presents evidence of its usefulness.     

基于Mutli—Agent的图像理解

具有专家系统外壳的图像理解软件的开发一直是一个重要的尚未解决的研究课题。我们的研究就是试图解决此问题：首先将agent构造环境扩展为一个mutli-agent的图像理解系统,而此系统是知识入口的开发工具,它为用户提供一个类似于专家系统外壳的界面。这样一方面能加速图像理解系统的开发,另一方面方便那些缺乏图像理解知识的人开发图像理解应用程序。     

Forming processes control by means of artificial intelligence techniques

Forming processes are manufacturing processes that use force and pressure in order to modify the shape of a material part until obtaining the final product. The wide range of non-linear factors that drive this sort of processes make them very complex and extremely difficult to be controlled. Traditional control techniques, like PID controllers, have not offered a reliable solution when global control has been pursued and the figure of the operator still remains present in most of the forming facilities. On the other hand, although operators have demonstrated to be a very successful strategy when controlling this type of processes, the actual market evolution towards the fabrication of more complex parts, made of lower formability materials at higher production rates, is decreasing their capacity of reaction when solving the daily problems. Thus, the development of new global control systems based not on traditional control techniques and mathematical models but on the control strategy that has been used successfully for many years, the control through the experience and knowledge is now even more necessary. In the present work, an intelligent control system based on one of the main techniques within the artificial intelligence, expert systems, has been developed. The main purpose of this intelligent control system is to emulate the decisions that expert operators take but in a quicker and more reliable way. The developed intelligent control system has been installed in a blanking facility and very good results have been achieved.     

基于Multi Agent的图象理解

最近十多年来,具有专家系统外壳的图象理解软件的开发一直是一个重要的研究课题。然而,多数基于知识的图象理解系统或者局限于诸如分割之类的特定操作上,或者局限于像理解建筑物的航空图象这样特定的应用。由于这些系统的目标和知识结构的局限性,因而不能将它们推广到其它领域。此研究就是试图解决此问题：首先将Agent构造环境扩展为一个Muliti Agent的图象理解系统,而此系统是知识入口的开发工具,它为用户提供一个类似于专家系统外壳的界面。这样一方面能加速图象理解系统的开发,另一方面方便那些缺乏图象理解知识的人开发图象理解应用程序。     

Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.     

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.     

Why evolutionary development of expert systems appears to work

Currently most expert systems are developed using an evolutionary method. The method has been criticised for its lack of scientific approach in problem solving. It is here argued that the method continues to be popular among expert systems builders because it can tackle four crucial problems, namely: (1) the problem of defining system requirements; (2) the problem of extracting expert knowledge; (3) the problem of understanding and structuring expert knowledge for machine manipulation; (4) the problem of maintaining the interest and enthusiasm of the domain experts. The method has some shortcomings, which calls for a disciplined style and control as well as supportive software tools.     

Expert systems for legal decision making

Abstract: AI technology is being used to develop expert systems that solve complex problems in the legal area. Most of these systems employ rules to describe the strategies and procedures used by litigators to analyze legal issues. The tasks performed by these systems include interpreting the law, anticipating the legal consequences of proposed actions, predicting the effects of changes in legislation, as well as analyzing and managing cases. The special characteristics of the legal domain cause certain problems for expert system builders. We discuss some of these problems and describe LDS and SAL, two expert systems we have developed for case evaluation and settlement in the product liability area. SAL (System for Asbestos Litigation) evolved from the earlier and more general expert system, LDS (Legal Decisionmaking System). We also describe XPL, an explanation facility we developed for use by SAL and other expert system applications.     

Using hybrid expert system approaches for engineering applications

In this paper, the use of hybrid expert system shells and hybrid (i.e., algorithmic and heuristic) approaches for solving engineering problems is reported. Aspects of various engineering problem domains are reviewed for a numer of examples with specific applications made to recently developed prototype expert systems. Based on this prototyping experience, critical evaluations of and comparisons between commercially available tools, and some research tools, in the United States and Australia, and their underlying problem-solving paradigms are made. Characteristics of the implementation tool and the engineering domain are compared and practical software engineering issues are discussed with respect to hybrid tools and approaches. Finally, guidelines are offered with the hope that expert system development will be less time consuming, more effective, and more cost-effective than it has been in the past.This paper is an attempt to provide some guidelines to expert system development based on limited experience with a handful of tools. It is not the intent of this paper to recommend or endorse the software, manufacturers, or organizations named within, nor to slight others not mentioned. We would appreciate feedback on omissions or oversights, so that more objective information about specific tools can be made public.Work performed while Aerospace Engineer, Artificial Intelligence Section, Mission Planning and Analysis Division, NASA/Johnson Space Center, Houston, TX.     

Classifying and detecting anomalies in hybrid knowledge-based systems

The increasing need for hybrid Knowledge-Based Systems (KBS) that accommodate more complex applications has led to the need for new verification concerns that are more specific to the hybrid representation using objects and rule-based inference. Traditionally, verification of expert systems has focused solely on rule-based inference systems. Hybrid KBSs present additional verification problems not found in traditional rule-based systems. This paper is an investigation into the anomalies that may be present in a hybrid representation that warrant detection for the verification of the KBS. Many anomalies are due to the interaction of the component parts of the hybrid KBS. For example, subsumption anomalies arise due to an interaction between inheritance of objects and rule-based inference. In this paper, we extend the context of subsumption anomalies and introduce additional types of anomalies that may be present in the KBS. The goal of this research is to make hybrid KBSs more reliable by detecting such anomalies.     

Professional negligence and financial‐legal expert systems: Architectures to enable the reasonableness defence

The legal implications of professionals relying on expert systems have been extensively studied by analysts who have considered the ‘macro’ issues such as whether products liability can be invited for loss resulting from errors in expert systems, and apportionment of liability. These macro‐analyses do not distinguish programming errors from what we call ‘errors of reasoning’. The latter errors may be considered to be advertent on the part of developers and/or user‐professionals; may potentially be directly compared with the reasoning ascribable to a reasonably competent professional; and cannot be removed merely by better quality assurance in the sense prevalent in software engineering. In this paper we consider the mechanisms by which a court may examine errors of reasoning in expert systems used to assist professionals working in certain areas of financial services ('the financial‐legal domain'). Our analysis of the ‘micro’ issues of liability shows that the advertent nature of errors of reasoning makes it difficult to argue that using the best possible quality assurance methods during development is sufficient to make the reliance on an expert system reasonable. However, certain valuable defences that are available to professionals acting unaided can be made out even when expert systems are employed as part of the reasoning process, though these defences will be disabled if certain conceptual structures of the knowledge of professionals are not distinctively maintained in the knowledge base. As such, opacity of a knowledge base assists, rather than hinders, a plaintiff in establishing professional negligence. The results of an analysis of the micro‐issues of liability have been applied by us in the design of a system for financial/tax planning. A case‐based reasoning approach is employed, deductive rules being employed strictly in a subsidiary role. Consequently, the primary justification for a decision is a ‘case’ ‐ an instance of problem‐solving by a professional ‐ that is likely to be accepted by the court as being representative of the actions of reasonably competent professionals.     

REDSIEX: A Cooperative Network of Expert Systems with Blackboard Architectures

The blackboard architecture, originally developed for the system that permits the comprehension of language, HEARSAY II, has later been used in a great variety of domains and in various environments for the construction of systems. From the classic architecture of HEARSAY II, many applications, generalizations, extensions and refinements have been developed. In this paper we present REDSIEX, (RED de SIstemas EXpertos) which is a network of expert systems within a blackboard architecture, for the cooperation solution of distributed problems. The REDSIEX system inherits various of the elements defined by the architecture of HEARSAY II and incorporates new components and organization. These produce a very characteristic and exclusive global work style in the solution of problems, within a conceptual framework of emergent control. The main structural and functional characteristics of REDSIEX are discussed.     

Basic theoretical results for expert systems. Application to the supervision of adaptation transients in planar robots

The objective of expert systems is the use of Artificial Intelligence tools so as to solve problems within specific prefixed applications. In the last two decades a great experimental effort together with some theoretical knowledge have been employed to investigate the completeness and consistency of knowledge-based systems and to clarify the structure of these systems. Nevertheless, there is often a gap in the formalism which allows the structuring of the expert system programming towards the expert system design. In the last years, a new field called Ontological Engineering, defined by the IEEE as “the field that establishes a set of concepts, axioms, and relationships that describe a domain of scientific or technological interest” is trying to fill this gap. The work presented here may be placed in this context. In particular, the paper deals with the development of an expert system valid to optimize the adaptation transients arising in adaptive control using a logic formalism previously described, providing good simulation results. Its structure is composed by a supervisor based on an expert network organization and designed to improve the transient performances in the adaptive control of a planar robot. Apart form the basic adaptation scheme consisting of an estimation algorithm plus an adaptive controller, two additional coordinated expert systems are used to update an adaptation gain and the sampling period with a master expert system coordinating both above expert systems.     

基于知识图谱的航空发动机PHM仿真验证平台设计

针对航空发动机PHM系统功能、性能验证要求高、验证内容多、验证业务复杂等实际问题,分析了国外先进航空发动机PHM系统开发验证情况及经验,提出了一种基于知识图谱技术的PHM仿真验证平台设计方法。该方法利用了知识图谱在具有的半结构化、高效性、直观等特点,实现了发动机PHM验证涉及到的故障模式、故障特征、算法模型及专家知识等大量信息知识的有效组织与管理。在此基础上,进一步面向发动机PHM验证数据量大、计算需求高等需求,采用大数据、数据挖掘、机器学习、云服务等技术,搭建了基于知识图谱的航空发动机PHM仿真验证平台,实现了数据挖掘与信息提取、专家知识获取、多层级融合诊断智能导向型推理等应用。最后,以某型发动机为对象,介绍了发动机PHM仿真验证情况。经实际验证结果分析,该文提出的PHM仿真验证平台能够有效解决航空发动机PHM系统可验证的历史故障样本少、验证功能单一等问题。     

定量问题求解专家系统——ESQPS

定量问题求解系统，是人工智能和专家系统研究领域的一个重要课题。本文介绍了ＥＳＱＰＳ－一个应用面向对象（ＯＯ）技术开发，可用于科学和工程技术领域中求解基本物理问题的专家系统，简述了该系统的知识表达和问题求解方法的设计思想和实现技术。     

Uncertainty management in expert systems using fuzzy Petri nets

The paper aims at developing new techniques for uncertainty management in expert systems for two generic class of problems using fuzzy Petri nets that represent logical connectivity among a set of imprecise propositions. One class of problems deals with the computation of fuzzy belief of any proposition from the fuzzy beliefs of a set of independent initiating propositions in a given network. The other class of problems is concerned with the computation of steady-state fuzzy beliefs of the propositions embedded in the network, from their initial fuzzy beliefs through a process called belief revision. During belief revision, a fuzzy Petri net with cycles may exhibit “limit cycle behavior” of fuzzy beliefs for some propositions in the network. No decisions can be arrived at from a fuzzy Petri net with such behavior. To circumvent this problem, techniques have been developed for the detection and elimination of limit cycles. Further, an algorithm for selecting one evidence from each set of mutually inconsistent evidences, referred to as nonmonotonic reasoning, has also been presented in connection with the problems of belief revision. Finally, the concepts proposed for solving the problems of belief revision have been applied successfully for tackling imprecision, uncertainty, and nonmonotonicity of evidences in an illustrative expert system for criminal investigation