空中交通管制系统效能评估研究

定义空中交通管制系统效能的概念和计算方法,提出适合中国空中交通管制特点的效能评估模型,并综合运用专家调查法和改进的层次分析法计算空中交通管制系统的效能值。在效能评估模型和评估算法的基础上开发效能评估系统,对系统效能影响参数的灵敏度进行分析,验证该评估系统的有效性。     

专家系统中多种知识表示方法的集成应用

专家系统的建立是以知识为基础的，采用何种知识表示方法在一定程度上决定了专家系统能否正确高效的运行。先综合比较了常见的几种知识表示方法，然后介绍了在“现役混凝土结构耐久性评估专家系统”1中集成应用框架、产生式、神经网络等多种知识表示方法的思路及解决方案，该系统的实际使用证明此法是可行和有效的。     

HypEs: an architecture for hypermedia-enabled expert systems

Expert systems and hypermedia constitute two important technologies for organizations to create, store, and manage information products. The purpose of our research is to develop an architectural blueprint for the construction of hypermedia-enabled expert systems. We propose an architecture termed HypEs (Hypermedia-enabled Expert System) for the development of media-rich expert systems. The integration of hypermedia technologies and expert systems can provide significant potential benefits by enabling the storage and manipulation of non-textual knowledge, enhancing the effectiveness of both knowledge acquisition from the sources of expertise and knowledge transfer to non-expert users. An experimental analysis that contrasts the hypermedia-enabled and text-restricted expert systems provides results that underscore the usefulness of hypermedia techniques in enhancing the effectiveness of expert systems in practical applications.     

On making expert systems more like experts

Abstract: Expert systems still lack the skill of an expert when it comes to providing explanations of the results of expert reasoning. This is because while such systems may implement knowledge which is sufficient to mimic the performance of an expert, they do not necessarily model the expertise upon which that performance is based. Such a model must include knowledge of that domain's terminology, knowledge of domain facts, and knowledge of problem-solving methods. The Explainable Expert Systems project has been exploring a new paradigm for expert system development that is intended to capture such missing knowledge and make it available for explanation. This paper will discuss the principles behind this paradigm and consider two systems that employ it.     

Designer®—Intelligent expert system for intelligent manufacturing systems design

This survey on expert systems activities and trends in Yugoslavia offers some results already obtained in the domain of manufacturing science and technology. In the scope of a long-term research project “Intelligent Manufacturing Systems (IMS)—Theory and Application” a Designer® Intelligent Expert System for mafacturing engineering has been proposed and partially developed. Designer® IES is based on new developed knowledge automata theory enhanced with cellular automata concept. Induction learning by analogy and Quasimorphism knowledge mapping from real world to model world is used to generate a reasoning structure. The Intelligent Expert System is divided into three main subsystems, with a very large knowledge base:

• •Product designer
• •Process Designer, and
• •Production Planning and Control Designer.

All these segments were developed in pilot versions of expert systems for specific groups of activities inside each of these three domains.     

Belief‐rule‐based expert systems for evaluation of e‐government: a case study

Little knowledge exists on the impact and results associated with e‐government projects in many specific‐use domains. Therefore, it is necessary to evaluate the efficiency and effectiveness of e‐government systems. Because the development of e‐government is a continuous process of improvement, it requires continuous evaluation of the overall e‐government system as well as evaluation of its various dimensions such as determinants, characteristics and results. E‐government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level. Expert systems are a candidate solution to evaluate such complex e‐government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways, challenging to formalize. This paper presents an approach that can handle uncertainty in e‐government evaluation: the combination of Belief Rule Base knowledge representation and Evidential Reasoning. This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System (BRBES) and implemented in the local e‐government of Bangladesh. The results have been compared with a recently developed method of evaluating e‐government, and it is demonstrated that the results of the BRBES are more accurate and reliable. The BRBES can be used to identify the factors that need to be improved to achieve the overall aim of an e‐government project. In addition, various ‘what if’ scenarios can be generated, and developers and managers can obtain a foretaste of the outcomes. Thus, the system can be used to facilitate decision‐making processes under uncertainty.     

Evaluating the application of augmented reality devices in manufacturing from a process point of view: An AHP based model

Augmented Reality (AR) systems in last few years show great potentialities in the manufacturing context: recent pilot projects were developed for supporting quicker product and process design, as well as control and maintenance activities. The high technological complexity together with the wide variety of AR devices require a high technological skill; on the other hand, evaluating their actual impacts on the manufacturing process is still an open question. Few recent studies have analysed this topic by using qualitative approaches based on an “ex post” analysis – i.e. after the design and/or the adoption of the AR system - for evaluating the effectiveness of a developed AR application. The paper proposes an expert based tool for supporting production managers and researchers in effectively evaluating a preliminary ex-ante feasibility analysis for assessing quantitatively most efficient single AR devices (or combinations) to be applied in specific manufacturing processes. A multi-criteria model based on Analytic Hierarchy Process (AHP) method has been proposed to provide decision makers with quantitative knowledge for more efficiently designing AR applications in manufacturing. The model allows to integrate, in the same decision support tool, technical knowledge regarding AR devices with critical process features characterizing manufacturing processes, thus allowing to assess the contribution of the AR device in a wider prospective compared to current technological analyses. A test case study about the evaluation of AR system in on-site maintenance service is also discussed aiming to validate the model, and to outline its global applicability and potentialities. Obtained results highlighted the full efficacy of the proposed model in supporting ex-ante feasibility studies.     

Collection, storage and application of human knowledge in expert system development

Abstract: Expert systems are programs that analyze data by mimicking the thought processes of an expert. Two expert systems were developed by the Reservoir Evaluation and Advanced Computational Techniques group to aid in oil prospecting for two New Mexico formations, leading to the development of a third customizable fuzzy expert system. Knowledge engineering is a major part of the development of these expert systems, in which expert knowledge is solicited, analyzed, converted to rules stored in a system's knowledge base, and used by the computer to produce expert judgment. Numerical versions of the rules are used to analyze data and produce an evaluation of the user's prospect. In addition, the knowledge base preserves expert knowledge for future workers. This is especially important in the petroleum industry, as there is a cyclical trend in employment relating to the price of oil, retirements and people leaving and entering the industry.     

A financial expert system demonstration for business students

Expert systems is a rapidly developing application of artificial intelligence technology for the capture and dissemination of human knowledge. A number of very practical implementations of knowledge bases have been achieved, using program shells which are quite accessible by people other than programmers. Since college business students have a high probability of encountering expert systems in the workplace, it is appropriate that their curriculum expose them to this expanding arena. This paper presents an expert system demonstration designed to provide a hands-on educational environment for allowing students to explore the capabilities of artificial intelligence in business organizations.     

复杂体制雷达辐射源信号特征的SPA-FAHP评估

针对复杂体制雷达辐射源信号（ARES）特征评估缺乏有效手段和层次分析决策(AHP)过多依赖专家经验的问题,提出了复杂体制雷达辐射源信号特征的集对模糊层次评价模型(SPA-FAHP)。通过引入三角模糊数对传统层次分析法进行改进,并对复杂体制雷达辐射源信号特征评价指标体系的指标权重作模糊层次分析,以解决对雷达信号大容量数据进行评估时评判者存在不确定或者模糊判断的问题;再引入集对分析(SPA)理论对传统层次分析的专家决策矩阵进行改进,并对特征评估决策矩阵作同一度分析,以解决传统层次分析决策时过多依赖专家经验的问题。最后联合指标权重矩阵和决策同一度矩阵实现对复杂体制雷达辐射源信号特征的综合评估。计算结果表明该模型有效可行,能够更客观地实现对复杂体制雷达辐射源信号特征的分析和评估。     

复杂电子信息系统效能评估方法综述

本文首先给出了效能以及效能评估的概念,并指出了效能评估与费效比、性能等的区别,同时也明确了对复杂电子信息系统进行效能评估的重要性与必要性。接着介绍美国工业界武器系统效能咨询委员会方法、层次分析法、模糊评价法、神经网络方法、专家调查法、多属性决策方法6种用于复杂电子系统效能评估的方法。最后对这6种效能评估方法进行了优缺点、适用性以及应用关键点的横向分析。     

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

面向对象模型对复杂结构知识的表示方法

传统关系型数据库在表示专家系统中具有嵌套、递归等复杂结构的知识时遇到了困难。对此，本文提出了一种采用对象模型的解决方案。该方案在关系数据库的基础上使用映射原理实现了一种面向对象的模型，利用该模型可以较好地在数据库中表示各类复杂结构的知识。该方案已经在地震预报专家系统中得到应用，并取得了较好的效果。     

Expert systems in manufacturing: Promises and perils

Expert systems play a key role as a tool to enhance productivity, improve quality, increase profits and capture expertise in many business and industrial environments. Although the potential of expert systems is well-understood, it has been a challenging, and often elusive, task to make an expert system operational in the “real-world” environment. One of the major reasons why many expert systems fail to be integrated into the operational environment is a lack of knowledge and techniques on how to test an expert system. The implications of an inadequately-tested expert system in a manufacturing environment are serious. In this paper, we take a look at the many applications of expert systems in manufacturing and utilize a three-pronged approach to develop robust and reliable expert systems.     

基于多层次灰关联分析的图象分割性能评估

图象分割是自动目标识别系统(ATRS)的关键部分,它是一种基本的图象分析技术,作为图象分析的重要步骤在许多图象应用分析中都是必不可少的,其结果直接影响到其后的信息处理过程.分割算法性能的评估是ATRS离线评估的重要组成之一.根据信息融合与系统工程思想,提出了基于多层次灰色关联度分析的图象分割性能评估模型,该方法综合信息融合、灰色系统和层次分析法等新兴学科精华而成,该方法既可以从整体上进行评估、又可以从某个侧面进行评估.文中给出了该方法同以往方法的实验对比,应用实例显示,它在现有文献的基础上提高了数据的离散性,便于区分不同分割算法的性能,克服了以往方法的奇异性和不灵敏性,它在分割算法的性能评估方面比其他方法更为有效、合理,从而使评判更易进行.     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.     

Towards a suite of quality metrics for KADS-domain knowledge

In this paper, the quality metrics suite for object oriented (OO) design is partially used as an initial concept to develop metrics for statically measuring quality of KADS-domain knowledge bases. KADS-domain knowledge bases have common characteristics with OO, and other distinct features that limit the usage of some OO quality metrics. Therefore, new sets of domain knowledge quality metrics are proposed. The proposed matrices are enriched with an automatic tool used to measure quality of real world expert systems. In order to assess the effectiveness of these proposed quality metrics, these metrics were applied on a sample of real world agriculture application domains developed by CLAES (The Central Laboratory of Agriculture Expert System). Finally, complete analysis of the results obtained due to applying these metrics is presented.     

第一级认知系统在基于专家系统入侵检测中的应用研究

随着恶意入侵计算机现象的日益严重,准确检测入侵的需求应运而生。虽然基于专家系统的入侵检测方法检测准确度很高,但专家知识的获取和规则的动态更新是两大难点。该文提出将第一级认知系统(CS-1)应用于基于专家系统的入侵检测。CS-1采用桶队算法解决了规则评价和规则竞争问题,利用遗传算法不仅去除了降低检测效率的规则,而且取代由遗传算法生成的更强的规则子集。文中对专家系统规则到CS-1分类器的转换及分类器的检测方法进行了实例说明,实验结果体现了该方法的有效性和检测的高效性。     

Design considerations for knowledge acquisition

Expert systems are commercially becoming more prominent and available as the breadth of applications continues to expand, eventually they could very well become the most integral part of an organization's normal operations. Knowledge acquisition is often found to be the major problem in the development of expert systems. This paper places in order the framework for proper knowledge acquisition to ensure that knowledge which is the key for any expert system is acquired effectively from an expert.