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.     

The design process of expert systems development: some concerns

Abstract: A discussion is presented of why some expert systems that organizations have developed are not successful. The concept of design process plays a very significant role at the outset of the expert system development process. This concept has not been the subject of much debate and attention in expert systems development. From the author's point of view, one of the main issues is how the designer (knowledge engineer) thinks about the design process. In general, the designer's process is influenced by the knowledge engineer's conception. This paper endeavors to disclose some of the main factors related to the knowledge engineer's conception of the design process and an attempt is made to put forward a conceptual model of the expert system design process. This conceptual model is an initial step towards a successful implementation of expert system projects.     

NUREST:一个实时专家系统开发工具

对实时专家系统的特点作了概要论述,将专家系统技术和实时控制技术相结合,设计并实现了应用于实时问题求解的实时专家系统开发工具NUREST。主要讨论了它的知识表示、实时控制机制、推理机制以及实现技术,并给出了开发的炼钢控制实时专家系统实例。     

A Software Tool for Constructing Traditional Chinese Medical Expert Systems

It is an urgent task to hnplemeut a lot of expert systems to capture the valuable expertise ofexperienced doctors of traditional Chinese medicine.In order to meet the needs,a software tool isdeveloped.It features a unified diagnosis model,a specially designed knowledge representationlanguage and an efficient but effective inference engine.To implement an expert system,it isonly necessary to input the expert's knowledge expressed in knowledge representation languagewithout the design of any additional software.The time and effort required for implementing anexpert system are thus greatly saved.The software is very compact and can run onmicrocomputers e.g.IBM-PC/XT.Two traditional Chinese medical expert systems have beensuccessfully implemented with the tool.     

Using natural language sources in model-based knowledge acquisition

This paper introduces a well defined co-operation between domain expert, knowledge engineer, and knowledge acquisition and transformation tools. First, the domain expert supported by a hypertext tool generates an intermediate representation from parts of authentic texts of a domain. As a side effect, this representation serves as human readable documentation. In subsequent steps, this representation is semi-automatically transformed into a formal representation by knowledge acquisition tools. These tools are fully adapted to the expert's domain both in terminology and model structure which are developed by the knowledge engineer from a library of generic models and with preparation tools.     

CROSPAL,software that uses agronomic expert knowledge to assist modules selection for crop growth simulation

Crop growth models are used for a wide range of objectives. For each objective a specific model has to be developed, because the reusability of a model is often limited by the necessity of a fundamental restructuring to adapt it to a different objective. To overcome this limitation, we developed a method to facilitate model restructuring by a novel combination of software technology with expert knowledge.This resulted in the decision-making software application CROSPAL (CROp Simulator: Picking and Assembling Libraries). CROSPAL includes (1) a library of processes each containing different modelling approaches for each crop physiological process and (2) a procedure based on expert knowledge of how to combine the different processes for the objective of the simulation.A brief overview of the state of the art in crop modelling is presented, followed by an account of the developed concept to improve flexibility in crop modelling considering expert knowledge. We describe the design of the software and how expert knowledge is integrated. The use of CROSPAL is illustrated for the modelling of crop phenology. We conclude that CROSPAL is a helpful tool to improve flexibility in crop modelling considering expert knowledge but further development and evaluation is required to extend its range of application to more processes and issues crop modelling is presently addressing.     

实现渔业资源评估专家系统的一种方法

本文介绍了一种用计算机专家系统实现我国东海渔业资源评估的方法。这一渔业资源评估专家系统是用面向对象的知识处理系统（ＯＫＰＳ）构造而成。ＯＫＰＳ是我们自主开发的一套可视化专家系统工具。本文着重阐述了ＯＫＰＳ系统和该专家系统的原理、模型选择的规则、专家知识库的构造与实现等问题。     

Visual programming environment for hybrid expert systems

This paper proposes an architecture for hybrid expert system development which combines expert problem-solving functions and other conventional computational functions by visual programming technology. The visual programming technique is used both for task-specific knowledge representation and for procedural programming for connecting functional components. In knowledge representation, knowledge is visually represented in the form of decision tables and decision trees. In procedural visual programming, each functional object is displayed as a box-shaped icon with accessible ports which are connected by wires on the graphic editor. Based on the architecture, an expert system shell for the classification task, called HOLON/VP(DT), is incorporated in a visual programming system called HOLON/VP. This paper briefly describes the tool and its evaluation based on some application systems developed with the tool.     

利用专家系统开发工具VP－EXPERT建造金属材料大气腐蚀防护专家系统的探索

依据金属大气腐蚀与防护知识、全国大气腐蚀网站积累的腐蚀数据、试验研究成果及专家经验，利用专家系统开发工具（外壳）ＶＰ－ＥＸＰＥＲＴ探索建造金属材料大气腐蚀防护智能辅助决策系统。作为计算机科学技术在我国腐蚀科学领域中应用的尝试，推动我国环境腐蚀科学的发展。     

Expert system for pests,diseases and weeds identification in olive crops

An expert system was developed with the aim of improving decision-making by olive oil growers. Knowledge was obtained from the literature and from experts. The knowledge was then represented in the knowledge base of the expert system in a series of IF–THEN rules. The system is supported by a data base containing information for the identification of 9 weeds, 14 insects and 14 diseases. The system is enhanced with 150 photos and drawings that assist the used in the identification process. The expert system was evaluated following the conventional expert system evaluation methodologies. According to the validation results the system was considered very satisfactory. The program can be used as an identification tool for farmers and technicians and for educational purposes.     

Knowledge map: An approach to knowledge acquisition in developing engineering expert systems

This paper addresses the critical issues of knowledge acquisition in developing knowledge-based expert systems for engineering tasks. First, it reviews the role of knowledge acquisition and its current practice in expert system development. Then, a new approach based on three stages of knowledge refinement is suggested to improve the process of knowledge acquisition. This approach, calledrule verification without rule construction, is proposed to allow knowledge engineers and domain experts to experience a more intimate and balanced role in developing intelligent systems. The communication tool developed for this concept is calledknowledge map, which provides a systematic way of indexing and quantifying a piece of knowledge in the problem space by defining important attributes as the axes of the map. This approach is demonstrated by constructing a twodimensional map for a knowledge-based engineering design system, IDRILL, which we are currently developing. Future expansions of this knowledge acquisition technique are summarized as the conclusions of this paper.This paper was presented in part at the 1986 ASME International Computers in Engineering Conference in Chicago, IL, and appeared in the proceedings of that conference.     

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.     

A knowledge sorting process for a product design expert system

Knowledge sorting is one way to organize the knowledge acquired from the domain expert(s) and various sources. Well-organized knowledge representation will make rule extraction much easier. This paper describes a knowledge sorting process that was developed to facilitate the rule extraction for a product design expert system. The process capitalizes on the relationships between design attributes and factors, dependent and independent variables, and consists of three stages: identification of knowledge sources, generation of taxonomic trees, and organization of acquired knowledge. An example applies the sorting process to the development of an expert system for the design of wood head golf clubs.     

IEADVISE — an undergraduate course-advising expert system in Industrial Engineering

Abstract: IEADVISE is an undergraduate course-advising expert system developed and used by the Department of Industrial Engineering at the University of Missouri-Columbia, USA. The motivation for this development was the need for an online system that would make undergraduate course-advising accessible at all times to the Industrial Engineering students. Expert System Environment (ESE), an expert system shell on the IBM mainframe, was chosen as the development tool. This paper describes the knowledge base, the expert system and its operation, and the verification and validation procedures. Results of the verification and validation performed to ensure accuracy and reliability will be discussed.     

Symbolical Reasoning about Numerical Data: A Hybrid Approach

By combining methods from artificial intelligence and signal analysis, we have developed a hybrid system for medical diagnosis. The core of the system is a fuzzy expert system with a dual source knowledge base. Two sets of rules are acquired, automatically from given examples and indirectly formulated by the physician. A fuzzy neural network serves to learn from sample data and allows to extract fuzzy rules for the knowledge base. A complex signal transformation preprocesses the digital data a priori to the symbolic representation. Results demonstrate the high accuracy of the system in the field of diagnosing electroencephalograms where it outperforms the visual diagnosis by a human expert for some phenomena.     

Automated Integration of External Databases: A Knowledge-Based Approach to Enhancing Rule-Based Expert Systems

Expert system applications in the biomedical domain have long been hampered by the difficulty inherent in maintaining and extending large knowledge bases. We have developed a knowledge-based method for automatically augmenting such knowledge bases. The method consists of automatically integrating data contained in commercially available, external, online databases with data contained in an expert system's knowledge base. We have built a prototype system, named DBX, using this technique to augment an expert system's knowledge base as a decision support aid and as a bibliographic retrieval tool. In this paper, we describe this prototype system in detail, illustrate its use, and discuss the lessons we have learned in its implementation.     

A novel clustering algorithm based on data transformation approaches

Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters’ coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.     

基于知识的飞行器外形设计系统研究

针对飞行器外形设计的复杂性、往复性特点,提出了基于知识的飞行器外形设计系统体系结构。利用飞行器方案设计专家系统推理的结果,开发了基于UG平台的飞行器外形设计系统,实现了飞行器外形的自动式建模和向导式建模。融合飞行器外形设计的设计规则和经验知识,设计了基于UG/KF的飞行器各部位的知识特征模型。由此设计完成的飞行器外形模型可脱离该设计系统进行修改设计,从而满足了重复设计的需要,提高了设计效率。