Knowledge acquisition and integration tools aiming to support managerial planning in Greek SMEs

Most existing expert systems are defined in structured task domains. However, many real-life decision tasks are novel, unstructured and consequential. To support these tasks, expert systems are needed which provide an integrated environment capable of capturing new knowledge by updating the existing knowledge base. This paper describes the incremental development process of an expert system, from the initial gathering of data up to the development of knowledge acquisition tools and knowledge integration methodologies. The expert system developed addresses managerial planning tasks of Greek small-to-medium sized enterprises (SMEs). The manager sets values for parameters specifying environmental and company characteristics. The expert system responds with suggestions on feasible tactics, objectives and strategies. To cope with the changes of planning situations and also to improve the integrity of the knowledge base as the manager gains experience, knowledge acquisition tools have been introduced. These knowledge acquisition tools, which are manipulated directly by the manager, provide the system with additional knowledge and validate the knowledge already embedded in the knowledge base.     

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.     

Design and implementation of an environmental decision support system

An environmental decision support system is a specific version of an environmental information system that is designed to help decision makers, managers, and advisors locate relevant information and carry out optimal solutions to problems using special tools and knowledge. The RAISON (Regional Analysis by Intelligent Systems ON microcomputers) for Windows decision support system has been developed at the National Water Research Institute, Environment Canada, over the last 10 years. It integrates data, text, maps, satellite images, pictures, video and other knowledge input. A library of software functions and tools are available for selective extraction of spatial and temporal data that can be analysed using spatial algorithms, models, statistics, expert systems, neural networks, and other information technologies. The system is of a modular design which allows for flexibility in modification of the system to meet the demands of a wide range of applications. System design and practical experiences learned in the development of a decision support system for toxic chemicals in the Great Lakes of North America are discussed.     

Expert systems applied to problems in geographic information systems: Introduction, review and prospects

This paper critically reviews current expert system developments relevant to geographic information systems, and identifies several research topics for application of expert system technology in geographic information systems. We have identified four major problem domains of geographic information systems in which expert system technology has been applied—automated map design, terrain/feature extraction, database management/user interface, and geographic decision support systems. Efforts in each problem domain are critically reviewed. Considering the accomplishments and shortcomings of efforts to date, we identify areas likely to gain importance in this field. Our view of these prospects is moderated by constraints of current technology and a realistic view of current efforts. Future research will place more emphasis on formal representation of both knowledge and uncertainty. Another future research area will be the development of advanced tools for geographic knowledge acquisition. Finally, better methods for working with large-scale geographic databases will be needed.     

Knowledge acquisition as a constructive modeling activity

Knowledge acquisition is a constructive modeling process, not simply a matter of “expertise transfer.” Consistent with this perspective, we advocate knowledge acquisition practices and tools that facilitate active collaboration between expert and knowledge engineer, that exploit a serviceable theory in their application, and that support knowledge-based system development from a life-cycle perspective. A constructivist theory of knowledge is offered as a plausible theoretical foundation for knowledge acquisition and as an effective practical approach to the dynamics of modeling. In this view, human experts construct knowledge from their own personal experiences while interacting with their social constituencies (e.g., supervisors, colleagues, clients patients) in their niche of expertise. Knowledge acquisition is presented as a cooperative enterprise in which the knowledge engineer and expert collaborate in constructing an explicit model of problem solving in a specific domain. From this perspective, the agenda for the knowledge acquisition research community includes developing tools and methods to aid experts in their efforts to express, elaborate, and improve their models of the domain. This functional view of expertise helps account for several problems that typically arise in practical knowledge acquisition projects, many of which stem directly from the inadequacies of representations used at various stages of system development. to counter these problems, we emphasize the use of mediating representations as a means of communication between expert and knowledge engineer, and intermediate representations to help bridge the gap between the mediating representations themselves, as well as between the mediating representations and a particular implementation formalism. © 1993 John Wiley & Sons, Inc.     

A data analysis decision support system for the carbon dioxide capture process

This paper presents the development process of an expert decision support system for pre-filtering and analysis of data from the carbon dioxide (CO₂) capture process. Chemical absorption has become one of the dominant CO₂ capture technologies because of its efficiency and low cost. Since the chemical absorption process consists of dozens of components, it generates more than a 100 different types of data. Monitoring the vast amount of data can be complex, and data filtering and analysis processes are desirable. Specifically, invalid data captured as the equipment is started and shut down need to be filtered, and the filtered data need to be analyzed for different purposes. The expert decision support system for data pre-filtering and analysis not only filters out invalid data using different expert rules, but it can also modify or reuse filtering settings, and export the filtered data to various file formats for further analysis. During development of the expert decision support system, knowledge acquisition was emphasized. The system development process incorporated various technologies including the model-view-control (MVC) design pattern, the embedded database technology, the Java event delivery techniques and the eXtensible Markup Language (XML). Some sample sessions from system executions and some results generated from pre-filtering the data will also be discussed.     

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.     

On the limits of expert systems and engineering models in process control

The review is based on an analysis of current literature of expert systems and of system engineering models in dynamic process control. It starts with an analysis of the mental operations and cognitive requirements needed for supervisory control. Mental models are discussed as a function of situational requirements as well as of personal strategies. Systems engineering models and expert systems are briefly described and their function as decision support tools evaluated. Criteria are the overall functionality, similarity of knowledge bases and reasoning strategies of the human and the support system, adaptability to the operator's skill level and self-explanation of the support system in the interaction mode. As a result, system engineering models are only of limited value for knowledge-based process control. Expert systems seem to be very valuable tools for augmenting human decision making in process control, if the interaction problem can be solved.     

面向农业领域的智能系统技术体系架构及其实现

阐述作者及团队对农业智能系统的技术体系架构取得的研究成果,包括运用专家系统、知识表示、推理机制、知识获取、开发平台,智能计算、机器学习、数据挖掘、本体论、人工生命等关键技术,探索在作物施肥、病虫害诊治、栽培管理,园艺以及畜禽水产养殖等方面,应用于决策支持、控制、预测、检索、仿真等,反映了智能信息技术面向农业领域,具有广阔的应用前景。     

A tool for task-based knowledge and specification acquisition

Knowledge acquisition has been identified as the bottleneck for knowledge engineering. One of the reasons is the lack of an integrated methodology that is able to provide tools and guidelines for the elicitation of knowledge as well as the verification and validation of the system developed. Even though methods that address this issue have been proposed, they only loosely relate knowledge acquisition to the remaining part of the software development life cycle. to alleviate this problem, we have developed a framework in which knowledge acquisition is integrated with system specifications to facilitate the verification, validation, and testing of the prototypes as well as the final implementation. to support the framework, we have developed a knowledge acquisition tool, TAME. It provides an integrated environment to acquire and generate specifications about the functionality and behavior of the target system, and the representation of the domain knowledge and domain heuristics. the tool and the framework, together, can thus enhance the verification, validation, and the maintenance of expert systems through their life cycles. © 1994 John Wiley & Sons, Inc.     

基于决策树的网络故障诊断专家系统模型

针对传统网络故障诊断专家系统在知识获取方面的固有不足,结合决策树算法和专家系统的特点,提出一种基于决策树的网络故障诊断专家系统模型,讨论网络故障诊断专家系统模型的推理机制和推测算法以及知识获取的实现方法。实践结果表明,模型在知识获取方面具有获取过程简单、计算复杂性低、适应性强的特点。     

基于知识的景象适配性分析决策系统

文章考察了目前选择景象匹配区的准则的缺陷,阐述了景象适配性分析方法应该综合考虑景象的独特性度量值、时效性特征、专家经验知识、末段飞行约束条件等因素。将基准图的特征结合匹配概率估计值表达成决策表的形式,基于粗糙集理论进行适配性分析系统的知识提取,融合专家经验、末段飞行约束条件形成知识库。然后结合模型方法库,通过不确定性推理机制,建立了基于知识的景象适配性分析决策系统。     

The role of expertise research and human factors in capturing, explaining, and producing superior performance

OBJECTIVES: The goal of this article is to identify some of the major trends and findings in expertise research and their connections to human factors. BACKGROUND: Progress in the study of superior human performance has come from improved methods of measuring expertise and the development of better tools for revealing the mechanisms that support expert performance, such as protocol analysis and eye tracking. METHODS: We review some of the challenges of capturing superior human performance in the laboratory and the means by which the expert performance approach may overcome such challenges. We then discuss applications of the expert performance approach to a handful of domains that have long been of interest to human factors researchers. RESULTS: Experts depend heavily on domain-specific knowledge for superior performance, and such knowledge enables the expert to anticipate and prepare for future actions more efficiently. Training programs designed to focus learners' attention on task-related knowledge and skills critical to expert performance have shown promise in facilitating skill acquisition among nonexperts and in reducing errors by experts on representative tasks. CONCLUSIONS: Although significant challenges remain, there is encouraging progress in domains such as sports, aviation, and medicine in understanding some of the mechanisms underlying human expertise and in structuring training and tools to improve skilled performance. APPLICATIONS: Knowledge engineering techniques can capture expert knowledge and preserve it for organizations and for the development of expert systems. Understanding the mechanisms that underlie expert performance may provide insights into the structuring of better training programs for improvingskill and in designing systems to support professional expertise.     

A GUI for Jess

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess.     

搅拌器选型专家系统的知识处理和系统实现

从知识获取,知识表达,采用模糊集理论处理领域中不确定性知识以及群组决策等方面,阐述了搅拌器选型专家系统知识处理的解决方案。由系统结构和操作过程,对整个专家系统如何实现进行了描述。     

An expert system for real-time fault diagnosis of complex chemical processes

This paper presents the development and implementation of an expert system for real-time fault diagnosis of chemical processes. The expert system is applied as a real-time computer aided decision support system, providing operation suggestions to help field operators when abnormal situations occur. The knowledge base structure, representation of knowledge, and access to expertise are technically considered. Industrial applications to the fluid catalystic cracking process in refinery indicate that the expert system diagnoses abnormal events efficiently and promptly.     

Towards knowledge-based systems in clinical practice: development of an integrated clinical information and knowledge management support system

Given that clinicians presented with identical clinical information will act in different ways, there is a need to introduce into routine clinical practice methods and tools to support the scientific homogeneity and accountability of healthcare decisions and actions. The benefits expected from such action include an overall reduction in cost, improved quality of care, patient and public opinion satisfaction. Computer-based medical data processing has yielded methods and tools for managing the task away from the hospital management level and closer to the desired disease and patient management level. To this end, advanced applications of information and disease process modelling technologies have already demonstrated an ability to significantly augment clinical decision making as a by-product. The wide-spread acceptance of evidence-based medicine as the basis of cost-conscious and concurrently quality-wise accountable clinical practice suffices as evidence supporting this claim. Electronic libraries are one-step towards an online status of this key health-care delivery quality control environment. Nonetheless, to date, the underlying information and knowledge management technologies have failed to be integrated into any form of pragmatic or marketable online and real-time clinical decision making tool. One of the main obstacles that needs to be overcome is the development of systems that treat both information and knowledge as clinical objects with same modelling requirements. This paper describes the development of such a system in the form of an intelligent clinical information management system: a system which at the most fundamental level of clinical decision support facilitates both the organised acquisition of clinical information and knowledge and provides a test-bed for the development and evaluation of knowledge-based decision support functions.     

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.     

从关系数据库中获取专家系统规则

获取知识是构造专家系统最重要的环节。论述了一种从关系数据库中获取专家系统规则的方法，它以集合论和概率论为基础，特别适用于决策和推量型专家系统。